Topics in Cognitive Science 10 (1):242-257 (2018)

Abstract
Causal queries about singular cases are ubiquitous, yet the question of how we assess whether a particular outcome was actually caused by a specific potential cause turns out to be difficult to answer. Relying on the causal power framework, Cheng and Novick () proposed a model of causal attribution intended to help answer this question. We challenge this model, both conceptually and empirically. We argue that the central problem of this model is that it treats causal powers that are probabilistically sufficient to generate the effect on a particular occasion as actual causes of the effect, and thus neglects that sufficient causal powers can be preempted in their efficacy. Also, the model does not take into account that reasoners incorporate uncertainty about the underlying general causal structure and strength of causes when making causal inferences. We propose a new measure of causal attribution and embed it into the structure induction model of singular causation. Two experiments support the model.
Keywords Bayesian modeling  Causal attribution  Causal reasoning  Computational modeling  Preemption  Singular causation
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DOI 10.1111/tops.12309
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References found in this work BETA

Causation, Prediction, and Search.Peter Spirtes, Clark Glymour & Richard Scheines - 1996 - British Journal for the Philosophy of Science 47 (1):113-123.
From Covariation to Causation: A Causal Power Theory.Patricia W. Cheng - 1997 - Psychological Review 104 (2):367-405.
Causes and Explanations: A Structural-Model Approach. Part I: Causes.Joseph Y. Halpern & Judea Pearl - 2005 - British Journal for the Philosophy of Science 56 (4):843-887.

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A Formal Apology for Metaphysics.Samuel Baron - 2018 - Ergo: An Open Access Journal of Philosophy 5.

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