I’m developing an epistemic theory of causality.

• This focuses on rational causal beliefs and their relation to evidence.
• It is analogous to an epistemic (i.e., Bayesian) theory of probability.
• According to this view, causal beliefs aren’t beliefs about some non-epistemic causal relation – they are a kind of belief, just as, under an epistemic interpretation, probabilistic beliefs are a kind of belief (degree of belief).
• The causal facts are those causal claims that any optimal causal epistemology would deem established on the basis of total evidence.

Relevant work includes:

Yafeng Shan, Samuel D. Taylor & Jon Williamson: Epistemic causality and its application to the social and cognitive sciences, in Y. Shan (ed.), Alternative approaches to causation: beyond difference-making and mechanism, Oxford University Press, 2024, pp. 241-277. . doi: 10.1093/oso/9780192863485.003.0010

Jon Williamson: Calibration for epistemic causality, Erkenntnis 86(4):941-960, 2021. doi: 10.1007/s10670-019-00139-w

Michael Wilde & Jon Williamson: Evidence and Epistemic Causality, in A. von Eye & W. Wiedermann (eds), Statistics and Causality: methods for applied empirical research, pp. 31-41. Wiley, 2016. ISBN: 978-1-118-94704-3

Jon Williamson: How can causal explanations explain? Erkenntnis 78:257-275, 2013. doi: 10.1007/s10670-013-9512-x

Federica Russo and Jon Williamson: Epistemic causality and evidence-based medicine, History and Philosophy of the Life Sciences 33(4):563-582, 2011.

Federica Russo and Jon Williamson: Interpreting causality in the health sciences, International Studies in the Philosophy of Science 21(2): 157-170, 2007.

Jon Williamson: Causal pluralism versus epistemic causality, Philosophica 77(1), pp. 69-96, 2006;

Jon Williamson: Dispositional versus epistemic causality, Minds and Machines 16, pp. 259-276, 2006;

Jon Williamson: Bayesian nets and causality: philosophical and computational foundations, Oxford University Press, 2005. Chapter 9.