The Logic of Counterfactuals and the Epistemology of Causal Inference

Fuente: arXiv
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Autore principale: Lin, Hanti
Natura: Preprint
Pubblicazione: 2024
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author Lin, Hanti
author_facet Lin, Hanti
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2405_11284
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle The Logic of Counterfactuals and the Epistemology of Causal Inference
Lin, Hanti
Artificial Intelligence
Econometrics
Methodology
Other Statistics
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.
title The Logic of Counterfactuals and the Epistemology of Causal Inference
topic Artificial Intelligence
Econometrics
Methodology
Other Statistics
url https://arxiv.org/abs/2405.11284