This week’s conference on Causality and Explanation in the Sciences in Ghent was an unusually good academic meeting (link). Participants gathered from all over Europe, as well as a few from North America, Australia, and South Africa, to debate the logic and substance of causal interpretations of the world. Among other things, it provided all participants with a very good sense of the ideas about causation that are generating the most discussion today.
A general perception that emerges from the gestalt of papers at the conference is that there are three large focus areas in current research on scientific causation. First, there is interest in specifying what causal assertions and concepts mean in scientific explanations. What are the logical, conceptual, and pragmatic issues associated with causal assertions and explanations?
Second, there is a large body of work focusing on the methods we can use to support causal inference in the sciences. Every field of science produces volumes of data about variables and events over time. What methods exist to permit inferences about causal relationships among the observed variables and entities? This includes causal modeling statistical methods, but also comparative methods deriving from Mill’s methods of difference and similarity.
Third, there is a group of philosophers and scientists who are primarily interested in the ontology of causation in various parts of the sciences. How do various factors exercise causal powers in ecology, the social sciences, or complex systems? Researchers in these areas need provisional answers to questions raised by the first two groups, but their focus is on substantive causal processes rather than the logic of causal statements.
It is useful to inventory half a dozen approaches that were repeatedly cited. This survey is impressionistic but gives an idea of the current landscape.
The mechanisms approach. The idea that we can explicate causation through the idea of a mechanism has been rising in importance over the past twenty years. The idea here is that the fundamental causal concept is that of a mechanism through which X brings about or produces Y. This is argued to be key to causation from single-case studies to large statistical studies suggesting a causal relationship between two or more variables. Peter Hedstrom and other exponents of analytical sociology are recent voices for this approach for the social sciences, though expositions of this approach don’t usually go into the level of detail expected by philosophers like Woodward and Cartwright. An important paper by Peter Machamer, Lindley Darden and Carl Craver, “Thinking about Mechanisms”, sets the terms of current technical discussions; their view is referred to as the MDC theory. A common concern is that the approach hasn’t been as clear as it should be about what precisely a mechanism is. James Mahoney made this criticism in 2001 in “Beyond Correlational Analysis” reviewing Charles Ragin, Fuzzy-Set Social Science and Peter Hedstrom and Richard Swedberg, Social Mechanisms: An Analytical Approach to Social Theory (link), and we still need a more generally recognized specification of the idea. (See an earlier post on this approach; link.)
Jim Woodward is perhaps the leading exponent of the manipulability (or interventionist) account. He develops his views in detail in his recent book, Making Things Happen: A Theory of Causal Explanation. The view is an intuitively plausible one: causal claims have to do with judgments about how the world would be if we altered certain circumstances. If we observe that the concentration of sulphuric acid is increasing in the atmosphere, we might consider the increasing volume of H2SO4 released by coal power plants from 1960 to 1990. And we might speculate that there is a causal connection between these facts. A counterfactual causal statement holds that: If X (increasing emissions) had not occurred, then Y (increasing acid rain) would not have occurred. The manipulability theory adds this point: if we could remove X from the sequence, then we would alter the value of Y. And this in turn makes good sense of the ways in which we design controlled experiments.
Difference-making. Another strand of thinking about causation focuses on the explanations we are looking for when we ask about the cause of some outcome. Here philosophers note that there are vastly many conditions that are causally necessary for an event but do not count as being explanatory. Lee Harvey Oswald was alive when he fired his rifle in Dallas; but this doesn’t play an explanatory role in the assassination of Kennedy. Crudely speaking, we want to know which causal factors were salient; which factors made a difference in the outcome. Michael Strevens provides a detailed and innovative explication of this set of intuitions in his recent book Depth: An Account of Scientific Explanation, where he introduces his theory of “Kairetic” explanation.
Contrastive analysis as a theory of explanation. When we seek an explanation of something, we generally have something specific in mind: why X rather than X’? And an explanation that keys off the wrong contrast will fail, even though its premises are correct. Bas van Fraassen (1980), The Scientific Image, is often cited in this context. A conference participant, Petri Ylikoski, develops a contrastive counterfactual theory in his dissertation (link). This body of work seeks to clarify pragmatic issues concerning explanation, including understanding and explanatory relevance. If we ask for an explanation for why X occurred, we are usually presupposing a question like this:Why did X occur [rather than Y]?
- Why is John carrying his umbrella [rather than not]?
- Why is John carrying his umbrella [rather than his raincoat]?
- Why is John carrying his umbrella [rather than his assistant Harry]?
These all demand different answers:
- Because he expects rain;
- Because it is too warm for a raincoat;
- Because Harry is carrying three heavy suitcases.
Causal modeling theory. This topic refers to the large body of statistical theory devoted to identifying potential causal relationships among observable variables in a large data set. Hubert Blalock is a founder of this approach (Causal Inferences in Nonexperimental Research; 1964) with his statistical models for causal path analysis. (Here is a short account of the history of path analysis in genetics.) Judea Pearl has contributed a great deal to the method of structural equation modeling (SEM) in Causality: Models, Reasoning and Inference and elsewhere. Here is a handbook article in which he explains the method and its causal relevance (link). Pearl maintains a research blog on causality here. Granger causalityis a specific technique for assessing causal relationships within time series data: X Granger-causes Y if variations in X and Y together do a better job of predicting Y than variations in Y by itself.
Prior foundations of philosophical theories of causation. Two older discussions of causality also received some notice in these papers: J. L. Mackie on INUS conditions and causal fields (The Cement of the Universe: A Study of Causation) and Wesley Salmon on the causal structure of the world (Scientific Explanation and the Causal Structure of the World).
Nancy Cartwright’s “Causation: One Word, Many Things” provides a very good contemporary review of the varieties of approaches that are currently being taken to the idea of causation (link).
Much of the intellectual vitality of this group of philosophers is captured in the major work recently edited by Phyllis McKay Illari, Federica Russo, and John Williamson, Causality in the Sciences. The book contains a very wide range of disciplines and approaches in its treatment of the topic.