2023/09/12 by Lucas de Lara, de Lara, Lucas · 1 citation
Computer Science · Decision Sciences · Mathematics · #Advanced Causal Inference Techniques #Bayesian Modeling and Causal Inference #FOS: Mathematics #Multi-Criteria Decision Making #Statistics Theory (math.ST)
paper · pdf · doi:10.48550/arxiv.2309.05997
openalex publication_date 2023/09/12 · openalex created_date 2023/09/14 · openalex updated_date 2026/07/28
Most of the scientific literature on causal modeling considers the structural framework of Pearl and the potential-outcome framework of Rubin to be formally equivalent, and therefore interchangeably uses do-interventions and the potential-outcome subscript notation to write counterfactual outcomes. In this paper, we agnostically superimpose the two causal models to specify under which mathematical conditions structural counterfactual outcomes and potential outcomes need to, do not need to, can, or cannot be equal (almost surely or law). Our comparison reminds that a structural causal model and a Rubin causal model compatible with the same observations do not have to coincide, and highlights real-world problems where they even cannot correspond. Then, we examine common claims and practices from the causal-inference literature in the light of these results. In doing so, we aim at clarifying the relationship between the two causal frameworks, and the interpretation of their respective counterfactuals.