Organizers: Phyllis McKay Illari, Federica Russo, Jon Williamson, Erik Weber, Julian Reiss
Henk de Regt, Daniel Little, Michael Strevens, Mauricio Suarez and James Woodward.
Causality and causal inference play a central role in the sciences. Explanation is one of the central goals of scientific research. And scientific explanation requires causal knowledge. At least, these are well-known tenets in present-day philosophy of science.
In this conference, we aim to bring philosophers and scientists together to discuss the relation between causality and explanation.
Even though the view that explanation requires causal knowledge is widespread, some accounts of explanation present themselves as a-causal or even as non-causal. Kitcher’s unificationism had it that causal relations are epistemically dependent on explanatory relations, not vice versa. In the mechanistic framework, interlevel explanation is said to be constitutive, not causal. Other accounts of explanation are primarily functional. What is the precise relation between causal and a- or non-causal accounts of explanation?
Relatedly, one of the close relatives of explanation is understanding. But what is the precise relation between explanation and understanding? And what is the role of causation herein?
But wait a minute. There is no consensus as to what causation is. Probabilistic, mechanistic, interventionist, and other accounts are available on the market and it is still an interesting and open question how precisely they relate to each other and how this bears upon the problem of scientific explanation.
Are causality and explanation the same across scientific disciplines? Is causality in physics the same as in psychology? Is causal discovery in biology the same as in economics? And is explanation in geology the same as in chemistry? Mathematics seems to be devoid of causation. Does that mean that it is also devoid of explanation? And is there a place for causation in technological explanation?
Our explanatory practices are partly determined by pragmatic considerations. What precisely do we want to explain, and what do we want to use our explanatory knowledge for? Do these pragmatic considerations influence our search for causal relations? Do they play a role, either implicitly or explicitly, in our algorithms for automated causal discovery (such as algorithms based on causal Bayes nets)?
We welcome contributions addressing these and other questions.
- How is causality related to explanation? Is all explanation causal?
- Which accounts of causality best fit which accounts of explanation?
- Do different sciences demand different notions of causality and explanation?
- Which case studies shed most light on the uses of causality and explanation in the sciences?