Causal Models and the Logic of Counterfactuals

Causal models provide a framework for making counterfactual predictions, making them useful for evaluating the truth conditions of counterfactual sentences. However, current causal models for counterfactual semantics face limitations compared to the alternative similarity-based approach: they only apply to a limited subset of counterfactuals and the connection to counterfactual logic is not straightforward. This paper argues that these limitations arise from the theory of interventions where intervening on variables requires changing structural equations rather than the values of variables. Using an alternative theory of exogenous interventions, this paper extends the causal approach to counterfactuals to handle more complex counterfactuals, including backtracking counterfactuals and those with logically complex antecedents. The theory also validates familiar principles of counterfactual logic and offers an explanation for counterfactual disagreement and backtracking readings of forward counterfactuals.