On Proximal Causal Learning with Many Hidden Confounders

2020/12/12 by Nikos Vlassis, Vlassis, Nikos, Phil Hebda +7
Computer Science · Mathematics · #Advanced Causal Inference Techniques #Bayesian Modeling and Causal Inference #FOS: Computer and information sciences #Methodology (stat.ME) #Statistical Methods and Inference #stat.ME

paper · pdf · doi:10.48550/arxiv.2012.06725

arxiv created 2020/12/12 · openalex publication_date 2020/12/12 · arxiv updated 2020/12/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

We generalize the proximal g-formula of Miao, Geng, and Tchetgen Tchetgen (2018) for causal inference under unobserved confounding using proxy variables. Specifically, we show that the formula holds true for all causal models in a certain equivalence class, and this class contains models in which the total number of levels for the set of unobserved confounders can be arbitrarily larger than the number of levels of each proxy variable. Although straightforward to obtain, the result can be significant for applications. Simulations corroborate our formal arguments.

Citations

• Causal Inference Using Potential Outcomes
• The central role of the propensity score in observational studies for causal effects

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