Causal reasoning and backtracking

Philosophical Studies 147 (1):139 - 154 (2010)
Abstract
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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References found in this work BETA
Peter Gardenfors (1982). Imaging and Conditionalization. Journal of Philosophy 79 (12):747-760.
I. J. Good (1961). A Causal Calculus (I). British Journal for the Philosophy of Science 11 (44):305-318.
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James Joyce (2004). Bayesianism. In Piers Rawling & Alfred R. Mele (eds.), The Oxford Handbook of Rationality. Oxford: Oxford University Press. 132--155.

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Citations of this work BETA
Isabelle Drouet (2012). Causal Reasoning, Causal Probabilities, and Conceptions of Causation. Studies in History and Philosophy of Science Part C 43 (4):761-768.
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