Causal Explanation and Fact Mutability in Counterfactual Reasoning

Mind and Language 27 (1):55-85 (2012)
Abstract
Recent work on the interpretation of counterfactual conditionals has paid much attention to the role of causal independencies. One influential idea from the theory of Causal Bayesian Networks is that counterfactual assumptions are made by intervention on variables, leaving all of their causal non-descendants unaffected. But intervention is not applicable across the board. For instance, backtracking counterfactuals, which involve reasoning from effects to causes, cannot proceed by intervention in the strict sense, for otherwise they would be equivalent to their consequents. We discuss these and similar cases, focusing on two factors which play a role in determining whether and which causal parents of the manipulated variable are affected: Speakers' need for an explanation of the hypothesized state of affairs, and differences in the ‘resilience’ of beliefs that are independent of degrees of certainty. We describe the relevant theoretical notions in some detail and provide experimental evidence that these factors do indeed affect speakers' interpretation of counterfactuals
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Citations of this work BETA
Stefan Kaufmann (2013). Causal Premise Semantics. Cognitive Science 37 (6):1136-1170.
Similar books and articles
James Woodward (2011). Psychological Studies of Causal and Counterfactual Reasoning. In Christoph Hoerl, Teresa McCormack & Sarah R. Beck (eds.), Understanding Counterfactuals, Understanding Causation. Oxford University Press. 16.
Stathis Psillos (2007). Causal Explanation and Manipulation. In. In Johannes Persson & Petri Ylikoski (eds.), Rethinking Explanation. Springer. 93--107.
James M. Joyce (2010). Causal Reasoning and Backtracking. Philosophical Studies 147 (1):139 - 154.
Peter Menzies, Counterfactual Theories of Causation. Stanford Encyclopedia of Philosophy.
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