Causal Modeling Semantics for Counterfactuals with Disjunctive Antecedents


This paper applies Causal Modeling Semantics (CMS, e.g., Galles and Pearl 1998; Pearl 2000; Halpern 2000) to the evaluation of the probability of counterfactuals with disjunctive antecedents. Standard CMS is limited to evaluating (the probability of) counterfactuals whose antecedent is a conjunction of atomic formulas. We extend this framework to disjunctive antecedents, and more generally, to any Boolean combinations of atomic formulas. Our main idea is to assign a probability to a counterfactual ( A ∨ B ) > C at a causal model M by looking at the probability of C in those submodels that truthmake A ∨ B (Briggs 2012; Fine 2016, 2017). The probability of p (( A ∨ B ) > C ) is then calculated as the average of the probability of C in the truthmaking submodels, weighted by the inverse distance to the original model M. The latter is calculated on the basis of a proposal by Eva et al. (2019). Apart from solving a major problem in the research on counterfactuals, our paper shows how work in semantics, causal inference and formal epistemology can be fruitfully combined.



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Author Profiles

Giuliano Rosella
University of Turin
Jan Sprenger
University of Turin

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References found in this work

Causality.Judea Pearl - 2000 - Cambridge University Press.
Angellic Content.Kit Fine - 2016 - Journal of Philosophical Logic 45 (2):199-226.
The Logic of Conditionals.Ernest Adams, Ernest W. Adams, Jaakko Hintikka & Patrick Suppes - 1965 - Journal of Symbolic Logic 39 (3):609-611.
Interventionist counterfactuals.Rachael Briggs - 2012 - Philosophical Studies 160 (1):139-166.

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