Abstract
Causal accounts of scientific explanation are currently broadly accepted (though not universally so). My first task in this paper is to show that, even for a causal approach to explanation, significant features of explanatory practice are not determined by settling how causal facts bear on the phenomenon to be explained. I then develop a broadly causal approach to explanation that accounts for the additional features that I argue an explanation should have. This approach to explanation makes sense of several aspects of actual explanatory practice, including the widespread use of equilibrium explanations, the formulation of distinct explanations for a single event, and the tight relationship between explanations of events and explanations of causal regularities.
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Notes
This move to “broadly causal” explanations is different from the view that explanations cite causal laws primarily because the former allows causal regularity explanations that are not exceptionless. The present view also sanctions explanations that do not qualify as mechanistic explanations; an obvious example is equilibrium explanations.
I have here characterized depictions of causal patterns, but one might also ask what a causal pattern is (rather than what it takes to depict one). Causal patterns are patterns in the sense that they are relationships that generally obtain in a range of circumstances of some breadth, be it wide or narrow. This is why a depiction of a causal pattern must give information about the scope of that pattern. Causal patterns are causal in the sense that the pattern is ultimately due to causal connections. They are only broadly causal: the pattern may not consist in causal connections between successive events. Finally, notice I said that causal patterns generally obtain in a range of circumstances. This is because causal patterns can exist despite exceptions, even within the pattern’s range. I discuss the consequences of patterns allowing exceptions below.
This distinction between causal process explanations and causal patterns explanations is similar to Dretske’s (2004) distinction between triggering and structuring causes, but there is an important difference. Dretske’s triggering and structuring causes each may be part of the causal process that led to the event to be explained, whereas causal pattern explanations may cite features of the scenario that are not parts of the causal process. For instance, the ideal gas law expresses a structural feature shared by a wide variety of causal processes, including those with reverse causal dependencies, such as pressure causing a change in temperature and temperature causing a change in pressure.
See Dretske (1972) for an early treatment of contrastive statements.
See Potochnik (2011) for concerns about Strevens’ account of explanation that are related to the point developed here.
Some would attempt to characterize the difference as a difference in explanandum by claiming that natural selection (represented in the game theory model) explains why the sparrows evolved this trait and genetics and environmental conditions explain why the sparrows develop the trait. But this is simply a way to direct attention to one part or another of the causal history of the same event, namely that the sparrows have evolved to develop the trait in question. See also Potochnik (2010).
Above I said that a research program is often associated with choice of focal phenomena, hypotheses about types of causes or similarities with other phenomena, and a methodology to be employed. Research programs are also influenced by chance factors such as what equipment happens to be available and what techniques and target systems the researcher happens to be familiar with (Rob Skipper, in conversation). Such accidental circumstances thus also influence the explanations that are generated.
These must be objective probabilities, but otherwise, I expect it is irrelevant which probability interpretation is adopted. Notice that the requirement that the probabilities in question be approximately equal is vague. This is fitting, since it seems that the threshold of explanatory adequacy is also vague. Moreover, I expect the degree to which \(Pr(E|C_{expl})\) must approximate \(Pr(E|C)\) may vary somewhat in different circumstances of explanation. The degree of approximation required often allows for true causal influences of modest effect, and possibly even moderate effect, to be wholly neglected.
Along with my commitment to causal explanation, this distinguishes my condition of explanatory adequacy from Hempel’s requirement of deductive or statistical relevance. The condition of explanatory adequacy I propose in this section is designed for a different job. Additional differences will emerge as the discussion proceeds, including especially that the satisfaction of my proposed requirement does not require explicitly citing all significant factors.
No matter how complex and epistatic the genetic influences are, \([EA]\) is satisfied by the simple assumption of heritability. So long as the genetics in fact result in strong enough heritability for the assumption to be borne out, it is irrelevant to \([EA]\) that genetic interaction could have interfered.
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Acknowledgments
This paper was drafted during a Research Fellowship at the Michelle R. Clayman Institute for Gender Research at Stanford University, and it was completed with the help of a Charles Phelps Taft Summer Research Fellowship. The ideas have benefitted from the help of Michael Friedman, Peter Godfrey-Smith, Helen Longino, Elliott Sober, and Michael Strevens. Additionally, Chris Haufe, Robert Skipper, Elliott Sober, James Woodward, and the participants in a conference in honor of Elliott Sober’s 65th birthday, as well as several anonymous referees, have provided helpful feedback on earlier drafts of this paper.
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Potochnik, A. Causal patterns and adequate explanations. Philos Stud 172, 1163–1182 (2015). https://doi.org/10.1007/s11098-014-0342-8
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DOI: https://doi.org/10.1007/s11098-014-0342-8