The Logic of Counterfactuals and the Epistemology of Causal Inference

TL;DR

This paper explores the logical foundations of causal inference, critiques the role of the Conditional Excluded Middle in counterfactual semantics, and proposes an updated causal model combining Rubin's approach with causal Bayes nets.

Contribution

It introduces a new causal model that retains Rubin's strengths while removing reliance on the Conditional Excluded Middle, bridging logic and inference.

Findings

Rubin causal model presupposes CEM, which is debated in philosophy.

A new causal model combines Rubin's approach with causal Bayes nets.

Deductive logic and inductive inference are deeply interconnected.

Abstract

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.