Thinking about pandemic models

One thing that is clear from the pandemic crisis that is shaking the world is the crucial need we have for models that allow us to estimate the future behavior of the epidemic. The dynamics of the spread of an epidemic are simply not amenable to intuitive estimation. So it is critical to have computational models that permit us to project the near- and middle-term behavior of the disease, based on available data and assumptions.

Scott Page is a complexity scientist at the University of Michigan who has written extensively on the uses and interpretation of computational models in the social sciences. His book, The Model Thinker: What You Need to Know to Make Data Work for You, does a superlative job of introducing the reader to a wide range of models. One of his key recommendations is that we should consider many models when we are trying to understand a particular kind of phenomenon. (Here is an earlier discussion of the book; link.) Page contributed a very useful article to the Washington Post this week that sheds light on the several kinds of pandemic models that are currently being used to understand and predict the course of the pandemic at global, national, and regional levels (“Which pandemic model should you trust?”; (link). Page describes the logic of “curve-fitting” models like the Institute for Health Metrics and Evaluation (IHME) model as well as epidemiological models that proceed on the basis of assumptions about the causal and social processes through which disease spreads. The latter attempt to represent the process of infection from infected person to susceptible person to recovered person. (Page refers to these as “microfoundational” models.) Page points out that all models involve a range of probable error and missing data, and it is crucial to make use of a range of different models in order to lay a foundation for sound public health policies. Here are his summary thoughts:

All this doesn’t mean that we should stop using models, but that we should use many of them. We can continue to improve curve-fitting and microfoundation models and combine them into hybrids, which will improve not just predictions, but also our understanding of how the virus spreads, hopefully informing policy. 

Even better, we should bring different kinds of models together into an “ensemble.” Different models have different strengths. Curve-fitting models reveal patterns; “parameter estimation” models reveal aggregate changes in key indicators such as the average number of people infected by a contagious individual; mathematical models uncover processes; and agent-based models can capture differences in peoples’ networks and behaviors that affect the spread of diseases. Policies should not be based on any single model — even the one that’s been most accurate to date. As I argue in my recent book, they should instead be guided by many-model thinking — a deep engagement with a variety of models to capture the different aspects of a complex reality. (link)

Page’s description of the workings of these models is very helpful for anyone who wants to have a better understanding of the way a pandemic evolves. Page has also developed a valuable series of videos that go into greater detail about the computational architecture of these various types of models (link). These videos are very clear and eminently worth viewing if you want to understand epidemiological modeling better.Social network analysis is crucial to addressing the challenge of how to restart businesses and other social organizations. Page has created “A Leader’s Toolkit For Reopening: Twenty Strategies to Reopen and Reimagine”, a valuable set of network tools and strategies offering concrete advice about steps to take in restarting businesses safely and productively. Visit this site to see how tools of network analysis can help make us safer and healthier in the workplace (link). 

Social network analysis is crucial to addressing the challenge of how to restart businesses and other social organizations. Page has created “A Leader’s Toolkit For Reopening: Twenty Strategies to Reopen and Reimagine”, a valuable set of network tools and strategies offering concrete advice about steps to take in restarting businesses safely and productively. Visit this site to see how tools of network analysis can help make us safer and healthier in the workplace (link). 

Another useful recent resource on the logic of pandemic models is Jonathan Fuller’s recent article “Models vs. evidence” in Boston Review (link). Fuller is a philosopher of science who undertakes two tasks in this piece: first, how can we use evidence to evaluate alternative models? And second, what accounts for the disagreements that exist in the academic literature over the validity of several classes of models? Fuller has in mind essentially the same distinction as Page does, between curve-fitting and microfoundational models. Fuller characterizes the former as “clinical epidemiological models” and the latter as “infectious disease epidemiological models”, and he argues that the two research communities have very different ideas about what constitutes appropriate use of empirical evidence in evaluating a model. Essentially Fuller believes that the two approaches embody two different philosophies of science with regard to computational models of epidemics, one more strictly empirical and the other more amenable to a combination of theory and evidence in developing and evaluating the model. The article provides a level of detail that would make it ideal for a case study in a course on the philosophy of social science.

Joshua Epstein, author of Generative Social Science: Studies in Agent-Based Computational Modeling (Princeton Studies in Complexity), gave a brief description in 2009 of the application of agent-based models to pandemics in “Modelling to Contain Pandemics” (link). Epstein describes a massive ABM model of a global pandemic, the Global-Scale Agent Model (GSAM), that attempted to model the spread of the H1N1 virus in 1996. Here is a video in which Miles Parker explains and demonstrates the model (link). 

Another useful resource is this video on “Network Theory: Network Diffusion & Contagion” (link), which provides greater detail about how the structure of social networks influences the spread of an infectious disease (or ideas, attitudes, or rumors).

My own predilections in the philosophy of science lean towards scientific realism and the importance of identifying underlying causal mechanisms. This leaves me more persuaded by the microfoundational / infectious disease models than the curve-fitting models. The criticisms that Nancy Cartwright and Jeremy Hardie offer in Evidence-Based Policy: A Practical Guide to Doing It Better of the uncritical methodology of randomized controlled trials (link) seem relevant here as well. The IHME model is calibrated against data from Wuhan and more recently northern Italy; but circumstances were very different in each of those locales, making it questionable that the same inflection points will show up in New York or California. As Cartwright and Hardie put the point, “The fact that causal principles can differ from locale to locale means that you cannot read off that a policy will work here from even very solid evidence that it worked somewhere else” (23). But, as Page emphasizes, it is valuable to have multiple models working from different assumptions when we are attempting to understand a phenomenon as complex as epidemic spread. Fuller makes much the same point in his article:

Just as we should embrace both models and evidence, we should welcome both of epidemiology’s competing philosophies. This may sound like a boring conclusion, but in the coronavirus pandemic there is no glory, and there are no winners. Cooperation in society should be matched by cooperation across disciplinary divides. The normal process of scientific scrutiny and peer review has given way to a fast track from research offices to media headlines and policy panels. Yet the need for criticism from diverse minds remains.

New thinking about causal mechanisms

Anyone interested in the topic of causal mechanisms will be interested in the appearance of Stuart Glennan and Phyllis Illari’s The Routledge Handbook of Mechanisms and Mechanical Philosophy. Both Glennan and Illari have been significant contributors to the past fifteen years of discussion about the role of mechanisms in scientific explanation, and the Handbook is a highly interesting contribution to the state of the debate.

The book provides discussion of the role of mechanisms thinking in a wide range of scientific disciplines, from physics to biology to social science to engineering and cognitive science. It consists of four large sections: “Historical perspectives on mechanisms”, “The nature of mechanisms”, “Mechanisms and the philosophy of science”, and “Disciplinary perspectives on mechanisms.” Each section consists of contributions by talented experts on genuinely interesting topics.

A good introduction to the general topic of mechanisms is the introduction to the volume by Glennan and Illari, and more especially their article, “Varieties of mechanisms.” They directly confront one of the large issues in the field, the wide dispersion of definitions and applications of the idea of a causal mechanism. They correctly observe that the concept of mechanism is used fairly differently in various areas of science and philosophy, but they argue that there is a common core of elements that underlie most or all of these usages. The variety that exists is the result of differences in the nature of the phenomena across different areas of scientific investigation, and differences in methodology in use in various sciences. They provide a rather general definition of a mechanism:

A mechanism for a phenomenon consists of entities (or parts) whose activities and interactions are organized so as to be responsible for the phenomenon. (92)

They then attempt to provide a basis for classifying different kinds of mechanisms according to several different criteria. The dimensions of variation they identify include the kind of phenomenon produced, the kind of entities and activities constituting the mechanism, the way in which entities and activities are organized, and the etiology of the mechanism.

Also interesting is Petri Ylikoski’s contribution, “Social mechanisms.” Ylikoski structures his exposition of the theory of social mechanisms around the Coleman boat diagram (link). To provide a mechanism for a social phenomenon is to provide an account at the level of the actors of how a macro-level event or entity causally brings about another macro-level event or entity. Ylikoski insists that this is a matter of explanatory adequacy rather than reductive analysis, and is therefore not ontologically reductionist. But it does fundamentally imply that social mechanisms occur at the level of interactions among actors. In prior posts I have argued against this presupposition (link). I argue that it is perfectly intelligible to suggest that there are meso-level causal mechanisms. Ylikoski also underlines the affinity that exists between social mechanisms and agent-based modeling: a good ABM demonstrates the process through which a set of conditions at the micro-level aggregate to a certain kind of macro-level outcome. See this earlier post for a small amount of doubt about the adequacy of ABM models to perform this kind of social aggregation for realistic social scenarios; link. (Several of these points are developed in my New Directions in the Philosophy of Social Science.)

Povich and Craver address the topic of the relationship that exists between mechanisms, levels, emergence, and reduction in their contribution, “Mechanistic levels, reduction, and emergence”. This is a key question within the philosophy of social science. And the idea of  mechanism seems to have great relevance to the idea of various levels of phenomena. At the level of the organization we see, perhaps, chronic inefficiency in the use of certain kinds of resources. In searching for the mechanisms that cause this inefficiency we may choose to drop down a level and examine the incentives and constraints that guide the behavior of individuals within the organization. And we arrive at a theory of the individual-level mechanism that produces the meso-level outcome. This is a mechanism that falls along strut 3 of Coleman’s boat; it is an aggregative mechanism. But not all social mechanisms have this nature. If we want to know why rebellious segments of an agrarian society locate themselves in remote, mountainous areas, it is enough to know a few meso-level facts about the functioning of traditional military forces and the meso-level fact that mountainous terrain gives a tactical advantage to rebel commanders. This appears to be a meso-level mechanism from start to finish.

A particularly intriguing and original contribution is Abrahamsen, Sheredos, and Bechtel’s “Explaining visually using mechanism diagrams.” We tend to think of scientific explanations as mathematical demonstrations or text-based derivations of outcomes. Abrahamsen, Sheredos, and Bechtel point out that visual diagrams play a crucial role in the presentation of many scientific results; and these diagrams are not merely heuristic or illustrative. A visual presentation serves to designate how the hypothesized mechanism works: what its parts are, how the parts influence each other, and how the functioning of the mechanism over time produces the outcome in question. The authors make an admirable attempt to provide a philosophy-of-science analysis of the components and logic of a visual diagram as an expository device for presenting a causal mechanism or process. They highlight the logical problems of representing entities, spatial location, and temporal duration within a diagram in a way that permits the viewer to gain an accurate understanding of the hypothesized mechanism or process. And they note that it is a conceptually simple step to introduce computational modeling into the graphical representation described here, so the processes in question can step through their interactions on-screen.

Taken together, the essays collected here constitute a valuable contribution to the literature on mechanisms and explanation. The handbook also gives the reader a concrete experience of how deeply varied the mechanisms literature is, leading to very interesting questions about cross-disciplinary communication. It appears to be genuinely challenging to formulate an abstract analysis of the idea of a causal mechanism that will mean approximately the same thing to researchers trained within significantly different research traditions. Unlike many handbooks, this collection warrants reading cover to cover. Researchers who believe that the mechanisms approach provides a valid way of understanding the metaphysics of causal inquiry and explanation will find every article stimulating and helpful.

(Here are a couple of prior posts on the challenge of providing a classification scheme for social mechanisms; link, link.)

George and Bennett on case study methodology


Establishing causal relationships within the fabric of the social world is more challenging than in the biological or physical-chemical domains. The reasons for this difficulty are familiar — the high degree of contextuality and contingency that is characteristic of social change, the non-deterministic character of social causation, and the fact that most social outcomes are the result of unforeseen conjunctions of independent influences, to name several.

Alexander George and Andrew Bennett argue for the value of a case-study method of social research in Case Studies and Theory Development in the Social Sciences. The idea here is that social researchers can learn about the causation of particular events and sequences by examining them in detail and in comparison with carefully selected alternative examples.

Here is how they describe the case-study method:

The method and logic of structured, focused comparison is simple and straightforward. The method is “structured” in that the researcher writes general questions that reflect the research objective and that these questions are asked of each case under study to guide and standardize data collection, thereby making systematic comparison and cumulation of the findings of the cases possible. The method is “focused” in that it deals only with certain aspects of the historical cases examined. The requirements for structure and focus apply equally to individual cases since they may later be joined by additional cases. (67)

George and Bennett believe that the techniques and heuristics of the case study approach permit the researcher to arrive at rigorous and differentiated hypotheses about underlying social processes. In particular, they believe that the method of process-tracing has substantial power in social research, permitting the researcher to move from the details of a particular historical case to more general hypotheses about causal mechanisms and processes in other contexts as well (6). They discourage research strategies based on the covering-law model, in which researchers would seek out high-level generalizations about social events and outcomes: “highly general and abstract theories … are too general to make sharp theoretical predictions or to guide policy” (7). But they also note the limits of policy relevance of “independent, stable causal mechanisms” (7), because social mechanisms interact in context-dependent ways that are difficult or impossible to anticipate. It is therefore difficult to design policy interventions based on knowledge of a few relevant and operative mechanisms within the domain of behavior the policy is expected to govern, since the workings of the mechanisms in concrete circumstances are difficult to project.

Fundamentally they align with the causal mechanisms approach to social explanation. Here is how they define a causal mechanism:

We define causal mechanisms as ultimately unobservable physical, social, or psychological processes through which agents with causal capacities operate, but only in specific contexts or conditions, to transfer energy, information, or matter to other entities. In so doing, the causal agent changes the affected entity’s characteristics, capacities, or propensities in ways that press until subsequent causal mechanisms act upon it. (137)

And they believe that the case-study method is a suite of methodological approaches that permit identification and exploration of underlying causal mechanisms.

The case study approach – the detailed examination of an aspect of a historical episode to develop or test historical explanations that may be generalizable to other events – has come in and out of favor over the past five decades as researchers have explored the possibilities of statistical methods … and formal models. (5)

The case study method is designed to identify causal connections within a domain of social phenomena.

Scientific realists who have emphasized that explanation requires not merely correlational data, but also knowledge of intervening causal mechanisms, have not yet had much to say on methods for generating such knowledge. The method of process-tracing is relevant for generating and analyzing data on the causal mechanisms, or processes, events, actions, expectations, and other intervening variables, that link putative causes to observed effects. (214)

How is that to be accomplished? The most important tool that George and Bennett describe is the method of process tracing. “The process-tracing method attempts to identify the intervening causal process–the causal chain and causal mechanism–between an independent variable (or variables) and the outcome of the dependent variable” (206). Process tracing requires the researcher to examine linkages within the details of the case they are studying, and then to assess specific hypotheses about how these links might be causally mediated. 

Suppose we are interested in a period of violent mobilization VM in the countryside at time t, and we observe a marked upswing of religious participation RP in the villages where we have observations. We might hypothesize that the surge of religious participation contributed causally to the political mobilization that ensued. But a process-tracing methodology requires that we we consider as full a range of alternative possibilities as we can: that both religious and political activism were the joint effect of some other social process; that religious participation was caused by political mobilization rather than caused that mobilization; that the two processes were just contingent and unrelated simultaneous developments. What can we discover within the facts of the case that would allow us to disentangle these various causal possibilities? If RP was the cause of VM, there should be traces of the influence that VM exerted within the historical record — priests who show up in the interrogation cells, organizational linkages that are uncovered through archival documents, and the like. This is the work of process tracing in the particular case. And I agree with George and Bennett that there is often ample empirical evidence available in the historical record to permit this kind of discovery.

Finally, George and Bennett believe that process-tracing can occur at a variety of levels:

The simplest variety of process-tracing takes the form of a detailed narrative or story presented in the form of a chronicle that purports to throw light on how an event came about…. A substantially different variety of process-tracing converts a historical narrative into an analytical causal explanation couched in explicit theoretical forms…. In another variety of process-tracing, the investigator constructs a general explanation rather than a detailed tracing of a causal process. (210-211)

One of the strengths of the book is an appendix presenting a very good collection of research studies that illustrate the case study methodology that they explore. There are examples from American politics, comparative politics, and international relations. These examples are very helpful because they give substance to the methodological ideas presented in the main body of the book.

Mechanisms thinking in international relations theory

source: Alex Cooley, “America and Empire” (link)

One of the most fundamental ideas underlying the philosophy of social science expressed here and elsewhere is the view that social explanations should seek out the causal mechanisms that underly the social phenomena of interest. So now we need to be able to say a lot more about what social mechanisms are, and how they relate to each other. Quite a bit of my own thinking has been devoted to this subject, and in a recent post I proposed that it would be useful to begin to compile an inventory of social mechanisms currently in use in the social sciences (link). There I suggested that it would be useful to find a motivated way of classifying the mechanisms that we discover.

Interest in mechanisms is taking hold in some sub-disciplines of political science. An especially clear statement of the appeal of the mechanisms theory of explanation for political science is offered by Andrew Bennett in “The Mother of All Isms: Causal Mechanisms and Structured Pluralism in International Relations Theory” (link). (Bennett is also co-author with Alexander George of the excellent book on case-study methodology, Case Studies and Theory Development in the Social Sciences.) In the current article Bennett reviews the progression that has occurred in IR theory from positivism and the covering law model, to the idea of high-level “paradigms” of explanation, to the idea of a diverse set of causal mechanisms as the foundation of explanation in the field. He calls the latter position “analytic eclecticism”, and he argues that it is a powerful and flexible way of thinking about the processes and research questions that make up the subject matter of IR theory.

In order to advance the value of mechanisms theory for working political scientists, Bennett argues that it will be helpful to attempt to classify the large number of mechanisms currently in use in IR theory in terms of a small number of dimensions. He proposes two dimensions in terms of which to analyze social mechanisms, which can be summarized as content and structure. The content dimension asks the question, what substantive social entities or properties are invoked by the mechanism? And the structure dimension asks the question, what is the nature of the relationship invoked by the mechanism? He proposes three large types of content: material power, functional efficiency, and legitimacy. And he suggests that there are four basic structures that can be formed: agent to agent, structure to agent, agent to structure, and structure to structure. (Notice that this corresponds exactly to the four arrows in Coleman’s boat, including the Type 4 “structure to structure” connection.) Here is how Bennett motivates this classification scheme:

This tripartite division of categories of mechanisms usefully mirrors the three leading ‘isms’ in the IR subfield: (neo)realism (with a focus on material power); (neo)liberalism (institutional efficiency); and constructivism (legitimacy). It thereby provides a bridge to the vast literature couched in terms of the isms, preserving this literature’s genuine contributions toward better theories on mechanisms of power, institutions, and social roles. (472)

Here is the resulting classification of social mechanisms that Bennett offers:

Others have found this approach to be promising. Here is an elaboration on Bennett’s classification by Mikko Huotari at the Mercator Institute for China Studies in Berlin:

(Thanks for sharing this classification, Mikko.)

I agree with Andrew in thinking that it is useful to find a non-arbitrary way of classifying mechanisms. It is quite worthwhile to make a start at this project. I’m not yet fully persuaded, however, by either of the axes that he proposes.

First, the content axis seems arbitrary — legitimacy, material power, functional efficiency. Why choose these substantive characteristics rather than a dozen other possible content features? Is it simply that these correspond to the three primary “isms” of IR theory — neorealism, neoliberalism, and constructivism (as he suggests earlier; 472)? But the thrust of the first part of the paper is that the “isms” are an unsatisfactory basis for guiding explanation in international relations theory; so why should we imagine that they serve to identify the crucial distinctions in content among social mechanisms? Would the content categories look different if we were taking our examples from feminist sociology, the sociology of organizations, or theories of legislatures? Bennett doesn’t assert that these content categories are exhaustive; but if they are not, then somehow the tabulation needs to indicate that there is an extensible list on the left. And are these categories exclusive? Can a given mechanism fall both into the legitimacy group and the functional efficiency group? It would appear that this is possible; but in that case classification is difficult to carry out.

Second, the structure axis. Why is it crucial to differentiate mechanisms according to their place within an agent-structure grid? Why is this an illuminating or fundamental feature of the mechanisms that are enumerated? Would this dimension explode if we thought of social organization as a continuum from macro to meso to micro (along the lines of Jepperson and Meyer (link), as well as several earlier posts here (link))?

An early question that needs answer here is this: What do we want from a scheme of classification of social mechanisms? Should we be looking for a strict classification with exhaustive and mutually exclusive groupings? Or should we be looking for something looser — perhaps more like a cluster diagram in which some mechanisms are closer to each other than they are to others?


We do have several other examples to think about when it comes to classifying mechanisms. In an earlier post I discussed Craver and Darden’s account of mechanisms in biology, and highlighted the table of mechanisms that they provide (link). It is evident that the Craver-Darden table is much less ambitious when it comes to classification. They have loosely grouped mechanisms into higher-level types — adaptation, repair, synthesis, for example; but they have not tried to further classify mechanisms in terms of the levels of the entities that are linked by the mechanism. So they offer one dimension of classification rather than two, and they leave it entirely open that there may be additional types to be added in the future. This is a fairly unexacting understanding of what is needed for a tabulation of mechanisms.

In Dynamics of Contention McAdam, Tarrow and Tilly offer a sort of classification of their own for the kinds of mechanisms they identify. They propose three types of mechanisms — environmental, cognitive, and relational (kl 375):

  • Environmental mechanisms mean externally generated influences on conditions affecting social life. Such mechanisms can operate directly: For example, resource depletion or enhancement affects people’s capacity to engage in contentious politics (McCarthy and Zald, ed. 1987).
  • Cognitive mechanisms operate through alterations of individual and collective perception; words like recognize, understand, reinterpret, and classify characterize such mechanisms. Our vignettes from Paris and Greenwood show people shifting in awareness of what could happen through collective action; when we look more closely, we will observe multiple cognitive mechanisms at work, individual by individual. For example, commitment is a widely recurrent individual mechanism in which persons who individually would prefer not to take the risks of collective action find themselves unable to withdraw without hurting others whose solidarity they value – sometimes at the cost of suffering serious loss.
  • Relational mechanisms alter connections among people, groups, and interpersonal networks. Brokerage, a mechanism that recurs throughout Parts II and III of the book, we define as the linking of two or more previously unconnected social sites by a unit that mediates their relations with one another and/or with yet other sites. Most analysts see brokerage as a mechanism relating groups and individuals to one another in stable sites, but it can also become a relational mechanism for mobilization during periods of contentious politics, as new groups are thrown together by increased interaction and uncertainty, thus discovering their common interests.
This too is a one-dimensional classification. And it appears to be intended to be exhaustive and mutually exclusive. But it isn’t clear to me that it succeeds in classifying all the mechanisms we might want to bring forward. Once again, this strikes me as a good beginning but not an exhaustive grouping of all social mechanisms.

My own preliminary grouping of mechanisms has even less structure (link). It groups mechanisms according to the subject matter or discipline from which they have emerged. But this does not serve to shed light on how these examples are similar or different from each other — one of the key purposes of a classification.

I think this is a very useful research activity, and Andrew Bennett has done a service to the theory of social mechanisms in putting forward this effort at classification. Let’s see what other schemes may be possible as well. A good scheme of classification may tell us something very important about the nature of how causation works in the social world.

Are there meso-level social mechanisms?

It is fairly well accepted that there are social mechanisms underlying various patterns of the social world — free-rider problems, communications networks, etc. But the examples that come readily to mind are generally specified at the level of individuals. The new institutionalists, for example, describe numerous social mechanisms that explain social outcomes; but these mechanisms typically have to do with the actions that purposive individuals take within a given set of rules and incentives.

The question here is whether we can also make sense of the notion of a mechanism that takes place at the social level. Are there meso-level social mechanisms? (As always, it is acknowledged that social stuff depends on the actions of the actors.)

This question is analogous to two other similar issues in other special sciences:

  • Are there information-system level causal mechanisms in human cognition?
  • Are there cellular-level causal mechanisms in biological systems?

Or, to the contrary, are all mechanisms in sociology, cognition science, and biology properly understood to be carried out at the level of individuals, neurons, and biochemistry?

Here is my version of a definition of a causal mechanism (link):

A causal mechanism is (i) a particular configuration of conditions and processes that (ii) always or normally leads from one set of conditions C to an outcome O (iii) through the properties and powers of the events and entities in the domain of concern. 

And here is the definition offered by Doug McAdam, Sidney Tarrow, and Chuck Tilly in Dynamics of Contention:

Mechanisms are a delimited class of events that alter relations among specified sets of elements in identical or closely similar ways over a variety of situations. (kl 354)

We should begin by asking what it is that we are looking for. What would a meso-level mechanism look like?

Here is a start: it would be a linkage between two conditions or entities, each of which is itself a meso-level structure or entity. So a meso-level causal mechanism is one in which both C and O are meso-level entities or conditions and where C leads to O “always or normally”.

Earlier I argued that meso-level entities possess causal powers: regular dispositions to produce specified effects, grounded in the substrate of social activity (link). If some of those effects Oi are themselves meso-level outcomes or structures, then our question here is answered. Any pair {C,Oi} is itself a meso-level causal mechanism. If, on the other hand, the causal powers of meso-level entities are restricted to changes in the behavior of individuals, then meso-level mechanisms do not exist.

McAdam, Tarrow, and Tilly address a very similar question in Dynamics of Contention, and they argue for what they call “relational mechanisms”:

Relational mechanisms alter connections among people, groups, and interpersonal networks. Brokerage, a mechanism that recurs throughout Parts II and III of the book, we define as the linking of two or more previously unconnected social sites by a unit that mediates their relations with one another and/or with yet other sites. Most analysts see brokerage as a mechanism relating groups and individuals to one another in stable sites, but it can also become a relational mechanism for mobilization during periods of contentious politics, as new groups are thrown together by increased interaction and uncertainty, thus discovering their common interests. (kl 376)

Having formulated the question in these terms, it seems that we can provide a credible affirmative answer: it is possible to identify a raft of social explanations in sociology that represent causal assertions of social mechanisms linking one meso-level condition to another. Here are a few examples:

  • Al Young: decreasing social isolation causes rising inter-group hostility (link)
  • Michael Mann: the presence of paramilitary organizations makes fascist mobilization more likely (link)
  • Robert Sampson: features of neighborhoods influence crime rates (link)
  • Chuck Tilly: the availability of trust networks makes political mobilization more likely (link)
  • Robert Brenner: the divided sovereignty system of French feudalism impeded agricultural modernization (link)
  • Charles Perrow: legislative control of regulatory agencies causes poor enforcement performance (link)

We might also consider the possibility of compound meso-level mechanisms, in which M1 produces M2 which in turn produces M3. Does the sequence also qualify as a mechanism? That depends on the strength of the relationships that exist at each link; if the conditional probabilities of the links fall low enough, then the compound probability of the chain is no longer sufficient to satisfy condition (ii) above (“initial condition normally leads to the outcome”).

Essentially this question comes down to the tightness of the linkages that exist among the sub-components of social systems. If there are sub-components within bureaucracies that maintain their properties and are tightly linked to specified outcomes, then these can play a role within meso-level causal mechanism narratives. If, on the other hand, the effects of a given subcomponent of a social system vary widely over time and space, then that type of component does not play a useful role in a causal mechanisms analysis. So the question of how extensive meso-level causal mechanisms are is itself an empirical one; it depends on the specific features of the social world.

So it seems as though we can offer two related conclusions about the causal reality of meso-level entities: meso-level structures possess causal powers, and there are causal mechanisms that invoke meso-level entities as both input and output.

A causal narrative?

source: Edward Tufte,

In a recent post I referred to the idea of a causal narrative (link). Here I would like to sketch out what I had in mind there.

Essentially the idea is that a causal narrative of a complicated outcome or occurrence is an orderly analysis of the sequence of events and the causal processes that connected them, leading from a set of initial conditions to the outcome in question. The narrative pulls together our best understanding of the causal relations, mechanisms, and conditions that were involved in the process and arranges them in an appropriate temporal order. It is a series of answers to “why and how did X occur?” designed to give us an understanding of the full unfolding of the process.

A narrative is more than an explanation; it is an attempt to “tell the story” of a complicated outcome. So a causal narrative will include a number of causal claims, intersecting in such a way as to explain the complex event or process that is of interest. And in my view, it will be a pluralistic account, in that it will freely invoke a number of causal ideas: powers, mechanisms, necessary and sufficient conditions, instigating conditions, and so forth.

Here is how I characterized a historical narrative in New Contributions to the Philosophy of History:

What is a narrative? Most generally, it is an account of the unfolding of events, along with an effort to explain how and why these processes and events came to be. A narrative is intended to provide an account of how a complex historical event unfolded and why. We want to understand the event in time. What were the contextual features that were relevant to the outcome — the settings at one or more points in time that played a role? What were the actions and choices that agents performed, and why did they take these actions rather than other possible choices? What causal processes—either social or natural—may have played a role in bringing the world to the outcome of interest? (29)

We might illustrate this idea by looking at the approach taken to contentious episodes and periods by McAdam, Tarrow, and Tilly in Dynamics of Contention. In their treatment of various contentious periods, they break the given complex period of contention into a number of mechanisms and processes, conjoined with contingent and conjunctural occurrences that played a significant causal role in the outcome. The explanatory work that their account provides occurs at two levels: the discovery of a relatively small number of social mechanisms of contention that recur across multiple cases, and the construction of complex narratives for particular episodes that bring together their understanding of the mechanisms and processes that were in play in this particular case.

We think what happens within a revolutionary trajectory can better be understood as the result of the intersection of a number of causal mechanisms. We do not offer a systematic account of all such mechanisms and their interaction in a sample of revolutionary situations. Instead, we use a paired comparison of the Nicaraguan revolution of 1979 and the Chinese student rebellion of 1989 to zero in on one processes in particular: the defection of significant elements from a dominant ruling coalition. (kl 2465)

The narrative for a particular case (the Mau Mau uprising, for example) takes the form of a chronologically structured account of the mechanisms that their analysis identifies as having been relevant in the unfolding of the insurgent movement and the government’s responses. MTT give attention to “episodes” within larger processes, with the clear implication that the episodes are to some degree independent from each other and are amenable to a mechanisms analysis themselves. So a narrative is both a concatenated series of episodes and a nested set of mechanisms and processes.

Robert Bates introduces a similar idea in Analytic Narratives under the rubric of “analytic narrative”. The chief difference between his notion and mine is that his account is limited to the use of game theory and rational choice theory to provide the linkages within the chronological account, whereas I want to allow a pluralistic understanding of the kinds and levels of causes that are relevant to social processes.
Here is a brief account of what Bates and his collaborators mean by an analytic narrative:

The chapters thus build narratives. But the narratives are analytic narratives. By modeling the processes that produced the outcomes, we seek to capture the essence of stories. Should we possess a valid representation of the story, then the equilibrium of the model should imply the outcome we describe—and seek to explain. Our use of rational choice and game theory transforms the narratives into analytic narratives. Our approach therefore occupies a complex middle ground between ideographic and nomothetic reasoning. (12)

As have others, however, we seek to return to the rich, qualitative, and descriptive materials that narratives offer. And, as have others, we seek an explicit and logically rigorous account of the events we describe… We seek to locate and explore particular mechanisms that shape the interplay between strategic actors and that thereby generate outcomes. Second, most of these [other] literatures are structural: they focus on the origins and impact of alignments, cleavages, structures, and institutions. Our approach, by contrast, focuses on choices and decisions. It is thus more micro than macro in orientation. By delineating specific mechanisms and focusing on the determinants and impacts of choices, our work differs from our predecessors. (12-13)

A narrative typically offers an account of an historically particular event or process: the outbreak of a specific war, the emergence of ethnic conflict at a specific place and time, or the occurrence of a financial crisis. This places narratives on the side of particular social-science analysis. Is there a role for generalization in relation to narratives? I think that MTT would suggest that there is not, when it comes to large event groups like revolutions. There is no common template of revolutionary mobilization and regime collapse; instead, there are local and national interactions that constitute recurring mechanisms, and it is the task of the social scientist to discover the linkages and contingencies through which these various mechanisms led to revolution in this case or that. MTT try to find a middle ground between particularity and generalization:

Have we only rediscovered narrative history and applied to it a new, scientistic vocabulary? We think not. While convinced of the futility of deducing general covering laws of contention, we think our program — if it succeeds — will uncover recurring sets of mechanisms that combine into robust processes which, in turn, recur over a surprising number and broad range of episodes. (kl 3936)

In my view, anyway, a narrative describes a particular process or event; but it does so by identifying recurring processes, mechanisms, and forces that can be discerned within the unfolding of the case. So generalizability comes into the story at the level of the components of the narrative — the discovery of common social processes within the historically unique sequence of events.

“How does it work” questions

Source: Karl Ove Moene in Alternatives to Capitalism, p. 85

One of the strengths of the causal-mechanisms approach to social explanation is how it responds to a very fundamental aspect of what we want explanations to do: we want to understand how something works. And a mechanisms account answers that question.

Let’s consider an example in detail. Suppose we observe that worker-owned cooperatives (WOC) tend to respond differently to price changes for their products than capitalist-owned firms (COF) when it comes to production decisions. The WOC firm will conform to this rule: “The higher the output price, the lower will be the supply” (85), whereas the COF firm will increase employment and supply. This is referred to as the Ward problem.

We would like to know how that comes about; what are the organization’s processes and interactions that lead to the outcome. This means that we need to dig deeply into the specific processes that lead to production and employment decisions in both kinds of enterprises and see how these processes lead to different results.

The key part of the explanation will need to involve an analysis of the locus of decision-making that exists within the enterprise, and a demonstration of how the decision-making process in a WOC leads to a different outcome from that involved in a COF.

Here is how Karl Ove Moene analyzes this problem in “Strong Unions or Worker Control?” (Alternatives to Capitalism).

A production cooperative with worker control is defined somewhat restrictively as follows:

  1. Productive activities are jointly carried out by the members (who in this case are the workers).
  2. Important managerial decisions reflect the desires of the members, who participate in some manner in decision making.
  3. The net income (income after expenses) is divide among the members according to some formula.
  4. The members have equal rights, and important decisions are made democratically by one person, one vote. (84)

A capitalist firm acts differently:

  1. Productive activities are carried out by wage laborers and directed by management controlled by the owners.
  2. Important managerial decisions reflect the desires of the owners of the enterprise.
  3. Producers are paid a wage set by the labor market. The net income is assigned as profits to the owners.
  4. Producers have no right of decision-making in production decisions.

The assumption is that decision-makers in both settings will make decisions that maximize their income — in other words, narrow egoistic economic rationality. In the assumptions used here for the cooperative, this implies that decision-making will aim at adjusting employment and production to the point where “marginal productivity (VMP) equals the net income per member (NIM)” (85). These quantities are represented in the graph above. Here is the reasoning:

What happens if the output price increases? In real terms, net income per member increases, because the fixed costs deflated by the output price decreases. Hence the NIM curve in Figure 5.1 shifts upwards, while the marginal productivity curve remains in place. As a consequence, the optimal number of members in the coop decreases and the firm’s supply decreases the higher the output price. [Hence the coop lays off excess workers.] (85-86)

(Actually, this is what should happen in the long term. Moene goes on to show that the coop would not behave this way in the short run; but he acknowledges that the economic reasoning is correct. So for the sake of my example, let’s assume that the coop behaves as Ward argues.)

The mechanism that distinguishes the behavior of the two kinds of firm is easy to specify in this case. The mechanism of individual decision-making based on rational self-interest is in common in the two types of firms. So the explanation doesn’t turn on the mechanism of economic rationality per se. What differs across the cases is the collective decision-making process and the interests of the actors who make the decisions in the two cases. The decision-making mechanisms in the two cases are reflected in principles 2-4. The coop embodies a democratic social-choice rule, whereas the capitalist firm embodies a dictatorship choice rule (in Kenneth Arrow’s sense — one actor’s preferences decide the outcome). A democratic decision about production levels leads to the reduction-of-output result, whereas a dictatorship decision about production levels leads to the increase-of-output result in these circumstances. And in turn, we are able to say that the phenomenon is explained by reference to the mechanism of decision-making that is embodied in the two types of firms — democratic decision making in the coop and autocratic decision making in the capitalist firm.

This is a satisfying explanation because it demonstrates how the surprising outcomes are the foreseeable results of the differing decision processes. It identifies the mechanisms that lead to the different outcomes in the different circumstances.
This example also illustrates another interesting point — that a given mechanism can be further analyzed into one or more underlying mechanisms and processes. In this case the underlying mechanism is the postulated model of action at the individual level — maximizing of self-interest. If we postulated a different action model — a conditional altruism model, for example — then the behavior of the system might be different.
(I think this is a valid example of a mechanisms-based social explanation. Others might disagree, however, and argue that it is actually a deductivist explanation, reasoning from general characteristics of the “atoms” of the system (individual actors) to aggregate properties (labor-expelling collective decisions).)

Mechanisms and powers

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source: William Bechtel, Discovering Cell Mechanisms: The Creation of Modern Cell Biology

The causal-powers approach to the understanding of causation is sometimes presented as an exclusive alternative to both traditional regularity theories and to more recent causal mechanism theories. In an earlier post I discussed Ruth Groff’s contributions to this topic. Here I would like to present a provocative view: that the causal mechanisms and causal powers are complementary rather than contradictory. The causal mechanisms theory benefits by being supplemented by a causal powers theory and the causal powers theory benefits by being supplemented by a causal mechanisms theory. In other words, the two theories are not exclusive alternatives to each other, but rather serve to identify different parts of the whole of causation.

The causal powers theory rests on the claim that causation is conveyed from cause to effect through the active powers and capacities that inhere in the entities making up the cause. The causal mechanisms theory comes down to the idea that cause and effect are mediated by a series of events or interactions that lead (typically) from the occurrence of the cause to the occurrence of the effect. In other words, cause and effect are linked by real underlying causal sequences (often repeatable sequences).

My thesis of the mutual compatibility of powers and mechanisms goes along these lines. If we press down on a putative mechanisms explanation, we are led eventually to postulating a set of causal powers that provide the motive force of the postulated mechanisms. But equally, if we press down on the claim that a certain kind of entity has a specified causal power or disposition, we are led to hypotheses about what mechanisms are set in play be its constituents so as to bring about this disposition.

Begin with a causal mechanism story:

  • C => {x happens bringing about y, bringing about z, bringing about u, which is E} => E

How is it that the sub-links of this chain of mechanism pieces happen to work to bring about their consequent? We seem to have two choices: We can look to discover a further underlying mechanism; or we can postulate that the sub-link entity or structure has the power to bring about its consequent. So if we push downward within the terms of a mechanism explanation, one way to close the story is by postulating a causal power at some level.

Now start with a causal power claim. Suppose we assert that:

  • Salt has the causal power of making H2O electrically conductive when dissolved.

Is this simply an unanalyzable fact about salt (or saline solution)? It is not; instead, we can look downward to identify the physical mechanisms that are brought into play when salt enters solution in H2O. That mechanism is well understood: the Na+ and Cl- ions created by the dissolution of salt permit free electrons to pass through the solution.

So we can explain the causal power by discovering the causal mechanism that gives rise to it; we explain links in the putative mechanism by alluding to the powers of the entities involved at that stage; and we can explain other things by referring to the causal powers that we have discovered to be associated with various kinds of things and structures.

If we take this set of possibilities seriously, then powers and mechanisms are answering different questions within the causal nexus. The reference to powers answers the question, “What does x do?”, while the reference to mechanisms answers the question, “How does x work?”

From a scientific point of view, it is always legitimate to ask how the powers of an entity or structure come to be in the natural world. What is it about the micro-structure of the thing in virtue of which the thing’s properties are established? In fact, this is one of the key intellectual challenges of the sciences. And this is a request for specification of some of the mechanisms that are at work. But likewise, it is always legitimate to ask what gives force to a given mechanism; and here we are eventually driven back to the answer, “some of the components of the mechanism have X, Y, Z powers to affect other entities” without further analysis within that particular explanation.

One might imagine that there are primitive causal powers — powers attached to primitive particles that have no underlying components or mechanisms.  We might begin to give a list of primitive causal powers: mechanical interactions among physical objects (transfer of momentum from one particle to another); electromagnetic properties inhering in one object and creating forces affecting other objects; gravitational forces among objects possessing mass; the causal interactions that occur within the central nervous system. And we might seek to demonstrate that all causal powers depend on combinations of these sorts of “primitive” causal powers — a kind of Hobbesian materialism.  But this is needlessly strenuous from a metaphysical point of view. Better is to consider the middle-level range of powers and mechanisms where we are able to move upwards and downwards in our search for underlying causal mechanisms and supervening causal powers.

This line of thought suggests that questions about the metaphysics of causation are perhaps less pressing than they are sometimes made out to be. A thing’s powers are not irreducible attributes of the thing; rather, they are the orderly consequence of the composition of the thing and the causal properties of those components and their interactions. It is hard to see that much turns on whether we think of the world as consisting of entities with powers, or as composites with system properties created by their components. The key question seems to be something like this: what is implied when we make a causal assertion? Both CP and CM agree that the core implication is the idea that one event, structure, or condition brought about the occurrence of another event, structure, or condition.  And the languages of both powers and mechanisms do a pretty good job of expressing what we mean in asserting this implication.

(John Dupré takes a similarly ecumenical view about several approaches to the theory of causation in a recent article, “Living Causes”, where he advocates for what he calls “causal pluralism”; link. He writes: “I believe that causality is a complex and diverse set of phenomena, and most or all of these accounts provide valuable and complementary perspectives on the topic. Such a pluralistic view is quite a common one among contemporary philosophers; however there are significant differences in the form that such pluralisms can take” (20). On the mechanisms side within the philosophy of biology is William Bechtel’s Discovering Cell Mechanisms: The Creation of Modern Cell Biology, who writes: “Beginning in the 1940s an initially small cadre of investigators who were pioneers in the modern discipline of cell biology began to figure out the biochemical mechanisms that enable cells to perform these functions. although miniaturized, the mechanisms they found to be operative in each cell are staggeringly complex” (1 ).)

Causal powers from a metaphysical point of view

ontology revisited

A number of scholars who are interested in causation have recently expressed new interest in the concept of causal powers. This makes sense in a very straightforward and commonsensical way. But it also raises some difficult questions about metaphysics: how are we to think about the underlying nature of reality such that things, events, or conditions have “causal powers”? These questions raise issues that a number of talented philosophers are now taking on in a systematic way. Particularly interesting are recent writings by Ruth Groff, who represents a wave of contemporary thinking in metaphysics that aims to revitalize portions of Aristotle’s views of causation in opposition to Hume’s.

Groff’s work on causal powers is sustained over a number of recent works, including especially her 2012 book Ontology Revisited: Metaphysics in Social and Political Philosophy (Ontological Explorations), her introduction and chapter in Greco and Groff, eds., Powers and Capacities in Philosophy: The New Aristotelianism, and her contribution to Illari, Russo, and Williamson, Causality in the Sciences. Groff emphasizes a broad clash of perspectives between a Humean theory of causation (“constant conjunction, no necessary relations among things or events”) and a neo-Aristotelian theory (“things have powers, powers underlie causal relations among things and events”). Here is how she and Greco put the perspective of the “New Aristotelianism” in Powers and Capacities:

Humeanism is now under serious pressure within analytic metaphysics. In particular, after having been dismissed for generations as so much antiquated animism, the loosely-Aristotelian theorizing of real causal powers has now come to be a major focus of research within the specialty. (kl 204)

Moreover, Groff believes that American social sciences are still largely in the grip of the Humean metaphysics.  In “Getting Past Hume” in Causality in the Sciences, she writes:

One can’t help but wonder what the outcome would actually be, were there to be a floor-fight on the question, i.e., a substantive debate within analytic philosophy and methodology of social science on the merits of Humean anti-realism about causality versus the merits of a powers-based, realist account of causality.

What is significant about all of this for my own argument is not so much that Humeanism continues to be the default ontology of especially American, often positivist, social science; but rather that it can be combined with the idea that it is not — i.e., with the idea that competing versions of regularity theory somehow differ in a deep way, or that it is possible to remain neutral on what causality is, whilst engaging in causal explanation.

This poses a stark contrast; either you are positivist, anti-realist, and Humean or you are anti-positivist, realist, and neo-Aristotelian.  However, it is worth observing that there seem to be two currents of realist thought that reject Humean causation, not just one: the powers ontology that Groff (and Mumford and Anjum in Getting Causes from Powers) advocate; and the causal-mechanisms approach that has been advocated by philosophers and sociologists such as Hedstrom, Elster, and Ylikoski under the broad banner of analytical sociology. One might take the view that the causal-mechanisms approach ultimately requires something like the powers ontology — “How else are we to account for the fact that sparks cause gasoline to explode?”; but on its face, these are two fairly independent realist responses to Hume. And certainly it is difficult to find a neo-Aristotelian predilection among the causal-mechanism advocates.

Groff takes up causal mechanisms theory in “Getting Past Hume” in  Causality in the Sciences. She concedes that this approach — in the hands of Jon Elster and in my own writings, for example — claims to be realist and anti-positivist, in that it rejects the notion that explanation depends on the discovery of general laws. But she doesn’t think that the causal-mechanisms approach actually succeeds in presenting a substantive alternative to the Humean framework on causation: “Upon closer examination, the mainstream mechanisms model is more of the same, metaphysically.”

Fleshing out her argument, she seems to be arguing that causal-mechanisms theory can either retreat to constant conjunction (at the level of the linkages of individual causal mechanisms) or it can press forward to a causal powers interpretation; there is no third possibility. “As with the other models, nothing on the mainstream mechanisms model is in a position of actually doing anything, in the sense of actively producing an effect. Thus here too, with an extra bit of ironic panache, the explanation-form functions as a delivery mechanism (no pun intended) for a Humean metaphysics.” And it is true that most definitions of causal mechanisms make some kind of reference to regularities and repeatability.

My own formulation of the mechanisms theory is one of the targets of Groff’s critique. And in fact I can reconstruct my reasons for thinking that mechanisms need to involve some kind of regularities; and I don’t think it implies a collapse onto Humean causation. (At one point I wanted to call them “pocket regularities”, to distinguish them from the grand social or psychological laws that Hempel and Mill seemed to want to discover.)  I wanted to assert that:

“M [information diffusion] is the mechanism connecting E [police beating] with O [rapid mobilization of an angry crowd]” is a description of a real underlying (perhaps unknown) causal process through which the features of E bring about the occurrence of O.

This is an ontological claim and it is a realist claim. But there is also an epistemic issue: How would we know that M is indeed such an underlying reality? It seems unavoidable that we would need to either produce empirical evidence supporting the conclusion that M frequently conveys these kinds of effects in these kinds of circumstances (the approach Tilly takes) or we need to have a theory of the mechanism which accounts for how it works to bring about the effect. The first boils down to a discovery of a limited set of regularities in a range of circumstances; the latter is a theoretical demonstration of how it works.  So this way of conceptualizing mechanisms does indeed invoke regularities of some sort.  However, it doesn’t agree with the Humean idea that causation is nothing but regularities or constant conjunction. The regularities that are invoked are symptoms of the underlying causal mechanism, not criterial replacements for the mechanism.

Moreover, the powers theory seems to be subject to the same possible objection: how do we know that lightning has the causal power of starting barns on fire, unless we have repeatedly observed the chain of events leading from lightning strike to blaze?

Another thing that demands more attention is an assumption about the implications of the “realism” of powers. What follows from the idea that things have real causal powers? Groff puts the view in these terms: to assert that powers are real is to assert that they are irreducible (kl 204). But that seems questionable. We may think that feudalism was real, while at the same time thinking that its properties and dynamics derived from more fundamental social relations that compounded to create the distinctive dynamics of feudalism. So it doesn’t seem that realists have to also accept the idea of irreducibility of the things about which they are realist. Or to put the point the other way around: the idea that realism implies irreducibility appears to also imply a fairly strong thesis about emergence. Groff returns to this set of ideas in Ontology Revisited, chapter three: “An emergent phenomenon (property or entity) is one that is not equivalent, ontologically, to the plurality of its parts.” And here too she emphasizes irreducibility. But, as Poe Yu-ze Wan shows in “Emergence a la Systems Theory: Epistemological Totalausschluss or Ontological Novelty?” in Philosophy of the Social Sciences, philosophers have differed on the question of whether “emergence” implies “irreducibility” (link). The theory of emergence offered by Mario Bunge does not require irreducibility.

One thing I particularly like about Groff’s work on causal powers is her persistence in working through the logical and conceptual implications of this field. She is painstaking in her effort to discover the implications of various parts of the several theories of causation (and freedom of the will in other essays); and this is exactly how we make progress on difficult philosophical issues like these.

Causal concepts

source: D. Little, “Causal Explanation in the Social Sciences,” Southern Journal of Philosophy (1995) (link)

It may be useful to provide a brief account of some of the key ideas that are often invoked in causal explanations in the social sciences. (Here is an earlier post that summarized some current issues in causation research; link. And here are several earlier articles on causal explanation; link, linklink.)

The general idea of a social cause (X causes Y) goes along these lines: X is a structure or feature of social life that varies across social settings and whose presence increases the likelihood of occurrence of Y. The presence of X (perhaps in the presence of Y and Z as well) contributes to processes leading to Y.

This simple formulation contains several hidden assumptions — most importantly, that outcomes have causes, that causes retain their characteristics over time and across instances, and that there are processes or dynamics within the domain of things and processes that convey with some form of necessity one set of circumstances and events onto another.

An example

For example, consider this hypothetical narrative describing a riot in a European city with a large community of impoverished immigrant people:

  • (C1) simmering resentment by immigrant youth of joblessness and low social esteem
  • (C2) heat wave creating discomfort and misery in crowded neighborhoods
  • (C3) chronic disrespectful and rough police treatment of immigrant youth
  • (I) forceful arrest of mis-identified young person in a city park, leading to serious injury of the youth
  • (O) several days of rioting occur
The associated causal hypothesis goes along these lines: In the context of simmering resentment by immigrant youth and a pattern of mistreatment by police, feelings in the community were unusually elevated by the heat wave. When the arrest occurred a small protest began in the park, which spread to other blocks in the city and eventuated in the burning of cars, smashing of shop windows, and multiple further arrests.

Conditions Ci are standing conditions that played a causal role in the occurrence of the riot. The arrest incident was the instigating event, the match that ignited the social “gasoline”. If any of C1, C2, C3 had been changed six months earlier, it is unlikely that O would have occurred. Each was necessary for I leading to O in the circumstances of the day.  If C1, C2, C3 are present, it is likely that some instigating event will occur in the normal hustle-bustle of urban life. I was the instigating condition. For researchers seeking general explanations of urban unrest, C1 and C3 appear to be strong candidates for common causes across many examples of urban riots. Two mechanisms are invoked here: a mechanism having to do with the individual’s propensity to engage in protest (“resentment and mistreatment elevates propensity to protest”) and a mechanism having to do with the spread of protest (“a small disturbance between a few teenagers and the police escalates through direct contact with other disaffected individuals through the neighborhood”).

Here are brief discussions of many of the concepts that are commonly invoked in discussions of social causation.

Causal narrative

An organized and temporally directed account of the occurrence of an event or change, identifying the conditions, circumstances, and events that were causally relevant to its occurrence. A narrative needs to provide empirical evidence for its empirical claims and theoretical justification for the causal mechanisms and processes it postulates.

Standing condition

A condition or circumstance that persists through an extended period of time and that serves as part of the necessary causal background of a given causal process or mechanism. Persistent racial isolation is a standing condition in many explanations of the effects of inner city poverty.

Instigating event

An instigating event is an occurrence, including change of state of some background property, that triggers a change in some other property or process. The early-morning arrest by patrons of a blind pig (unlicensed tavern) in Detroit was the instigating event of the 1967 Detroit riot/uprising.

Necessary condition

A condition that must be present in order for a given causal interaction to occur. “If X had not been present, the outcome O would not have occurred.”

Sufficient condition (conjunction of conditions)

A condition (or conjunction of conditions) whose presence suffices to bring about the outcome. “If X&Y&Z were present, then O would have occurred.”

Counterfactual statements

It is worth underlining the point that necessary and sufficient conditions invoke counterfactual statements: If X had not occurred, Y would not have occurred. The logic of counterfactuals (modal logic) has a controversial and unresolved history. But given that causal language always implies some kind of necessity, we cannot dispense with counterfactuals and still have an adequate causal vocabulary.

INUS condition (J. L. Mackie)

J.L. Mackie’s work on causation in The Cement of the Universe: A Study of Causation brought to closure a long line of thought about the logic of causal relations, culminating in his concept of INUS conditions. Consider this complex causal statement about the circumstances causing P:

‘All (ABC or DGH or JKL) are followed by P’ and ‘All P are preceded by (ABC or DGH or JKL)’ (Mackie, 62)

Mackie then defines an INUS condition:

Then in the case described above the complex formula ‘(ABC or DGH or JKL)’ represents a condition which is both necessary and sufficient for P: each conjunction, such as ‘ABC’, represents a condition which is sufficient but not necessary for P. Besides, ABC is a minimal sufficient condition:  none of its conjuncts is redundant: no part of it, such as AB, is itself sufficient for P. But each single factor, such as A, is neither a necessary nor a sufficient condition for P. Yet it is clearly related to P in an important way: it is an insufficient but non-redundant part of an unnecessary but sufficient condition: it will be convenient to call this … an inus condition. (62)

To simplify:

A is an INUS condition for P if for some X and Y, (AX v Y) is a necessary and sufficient condition for P, but A is not sufficient for P and X is not sufficient for P.

Causal mechanism

An interlocked series of events and processes that, once initiated by some set of conditions, [usually] brings about a given outcome O. The idea that there are real mechanisms embodied in the “stuff” of a given domain of phenomena provides a way of presenting causal relations that serves as a powerful alternative to the “regularity” view associated with Hume. “Poor performance on standardized tests by specific groups is caused by the mechanism of stereotype threat” (Claude Steele, Whistling Vivaldi: How Stereotypes Affect Us and What We Can Do (Issues of Our Time)). This mechanism is a hypothesized process within the cognitive-emotional system of the subjects of the test. (James Mahoney’s survey article on the mechanisms literature is a good introduction to the debate; link.)

Causal powers

The idea that certain kinds of things (metals, gases, military bureaucracies) have internal characteristics that lead them to interact causally with the world in specific and knowable ways. This means that we can sometimes identify dispositional properties that attach to kinds of things. Metals conduct electricity; gases expand when heated; military bureaucracies centralize command functions. (Harre and Madden, Causal Powers: Theory of Natural Necessity)

Probabilistic causal relation

A relationship between A and O such that the occurrence of A increases/decreases the likelihood of the occurrence of O. This can be stated in terms of conditional probabilities: P(O|A) ≠ P(O) [the probability of O given A is not equal to the probability of O]. For a causal realist, the definition is extended by a hypothesis about an underlying causal mechanism. [Smoking is a probabilistic cause of lung cancer [working through physiological mechanisms X,Y,Z]. This is equivalent to Wesley Salmon’s criterion of causal relevance (Scientific Explanation and the Causal Structure of the World).

Causal explanation of a singular event

When we are interested in the explanation of a single event, a causal narrative leading up to that event is generally what we are looking for. What led to the outbreak of World War I? Why did Khomeini come to power in Iran in 1979? There are generally two difficult problems facing a proposed causal-narrative explanation of a singular event. First, we need to somehow empirically validate the claims about causal mechanisms and processes that are invoked in the narrative. But since this is a singular event, we do not have the option of using experimental methods to empirically test the claim “X leads by mechanism M to Y” that the narrative proposes. This is one important reason why mechanism theorists have generally required that specified mechanisms have roughly similar causal properties in a range of circumstances. Circumstances embodying the core features of a public goods problem usually lead to elevated levels of free riding — whether in public radio fundraising, strikes, classroom discussions, or rebellions. Second, there is the problem of alternative realizability and multiple causal pathways leading to the same outcome. If the conditions leading to World War I were sufficiently ominous, then whether the assassination of the Archduke or some other event brought it about is of less explanatory importance. Given that potential instigating events occur with a certain probability, some event would have occurred within those few months that led to war. So it is better to identify the standing conditions that made war likely as the causes, rather than the assassination of the Archduke.

Generalizations about the causes of a kind of social entity or event

We are often interested in answering causal questions about classes of events: Why do peasant rebellions occur? Why does corruption rise to such high levels in many cities? Why do democracies not wage war against each other? Here we are looking for common conjunctions of causal factors that can be shown to be causally relevant in many such events. It is possible that we will discover that peasant rebellions do not have a single set of causal antecedents; rather there are multiple profiles of peasant rebellions, each with a set of causal conditions significantly different from the other profiles.

Methods of causal inquiry

How can social researchers identify causal relations among social events and structures? There are several groups of methods that social scientists and historians have employed: statistical-causal models, small-N models based on Mill’s methods of similarity and difference (link, link), and case studies and process-tracing methods through which researchers seek to identify and confirm causal relations in individual cases. In each case the method derives from fundamental ideas about the nature of causation: the idea that causal relations between several factors give rise to statistical regularities when we have a large number of cases; the idea that we can use the features of necessary and sufficient conditions to select cases in order to include or exclude certain factors as causally related to the outcome; and the idea that causal mechanisms and processes can often be observed fairly directly in the historical record (Alexander George and Andrew Bennett, Case Studies and Theory Development in the Social Sciences).

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