  Causal Modeling Semantics (CMS, e.g., Galles and Pearl 1998; Pearl 2000;
Halpern 2000) is a powerful framework for evaluating counterfactuals whose
antecedent is a conjunction of atomic formulas. We extend CMS to an evaluation
of the probability of counterfactuals with disjunctive antecedents, and more
generally, to counterfactuals whose antecedent is an arbitrary Boolean
combination of atomic formulas. Our main idea is to assign a probability to a
counterfactual (A v B) > C at a causal model M as a weighted average of the
probability of C in those submodels that truthmake A v B (Briggs 2012; Fine
2016, 2017). The weights of the submodels are given by the inverse distance to
the original model M, based on a distance metric proposed by Eva, Stern, and
Hartmann (2019). Apart from solving a major problem in the epistemology of
counterfactuals, our paper shows how work in semantics, causal inference and
formal epistemology can be fruitfully combined.
