The Logic of Counterfactuals and the Epistemology of Causal Inference
This work addresses a foundational problem in philosophy and causal inference, offering a novel integration of models that could impact both fields, though it appears incremental in refining existing frameworks.
The paper tackles the connection between the Rubin causal model, which underpins Nobel-winning causal inference methods, and the logical principle Conditional Excluded Middle (CEM) in counterfactual semantics, proposing an updated model that dispenses with CEM while retaining empirical successes. It argues for a deep interconnection between deductive logic and inductive inference, challenging a Quine-Putnam indispensability argument for CEM.
The 2021 Nobel Prize in Economics recognized an epistemology of causal inference based on the Rubin causal model (Rubin 1974), which merits broader attention in philosophy. This model, in fact, presupposes a logical principle of counterfactuals, Conditional Excluded Middle (CEM), the locus of a pivotal debate between Stalnaker (1968) and Lewis (1973) on the semantics of counterfactuals. Proponents of CEM should recognize that this connection points to a new argument for CEM -- a Quine-Putnam indispensability argument grounded in the Nobel-winning applications of the Rubin model in health and social sciences. To advance the dialectic, I challenge this argument with an updated Rubin causal model that retains its successes while dispensing with CEM. This novel approach combines the strengths of the Rubin causal model and a causal model familiar in philosophy, the causal Bayes net. The takeaway: deductive logic and inductive inference, often studied in isolation, are deeply interconnected.