Philosophical Studies 147 (1):139 - 154 (2010)

James M. Joyce
University of Michigan, Ann Arbor
I argue that one central aspect of the epistemology of causation, the use of causes as evidence for their effects, is largely independent of the metaphysics of causation. In particular, I use the formalism of Bayesian causal graphs to factor the incremental evidential impact of a cause for its effect into a direct cause-to-effect component and a backtracking component. While the “backtracking” evidence that causes provide about earlier events often obscures things, once we our restrict attention to the cause-to-effect component it is true to say promoting (inhibiting) causes raise (lower) the probabilities of their effects. This factoring assumes the same form whether causation is given an interventionist, counterfactual or probabilistic interpretation. Whether we think about causation in terms of interventions and causal graphs, counterfactuals and imaging functions, or probability raising against the background of causally homogenous partitions, if we describe the essential features of a situation correctly then the incremental evidence that a cause provides for its effect in virtue of being its cause will be the same.
Keywords Causal inference  Incremental evidence  Bayesian causal graph  Markov condition  Imaging  Backtracking counterfactual  Probabilistic causation
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Reprint years 2009, 2010
DOI 10.1007/s11098-009-9454-y
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References found in this work BETA

Causality: Models, Reasoning and Inference.Judea Pearl - 2000 - Cambridge University Press.
The Direction of Time.Hans Reichenbach - 1956 - Dover Publications.
Causation.David Lewis - 1973 - Journal of Philosophy 70 (17):556-567.
Causality.Judea Pearl - 2000 - Cambridge University Press.

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Imaging All the People.Hannes Leitgeb - 2017 - Episteme 14 (4):463-479.
Structural Counterfactuals: A Brief Introduction.Judea Pearl - 2013 - Cognitive Science 37 (6):977-985.

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