Causal inference with confounders missing not at random

2019/08/19 by S Yang, Shu Yang, L Wang +3 · 55 citations
Mathematics · #Advanced Causal Inference Techniques #Artificial intelligence #Causal inference #Causal model #Computer science #Confounding #Econometrics #Estimator #Inference #Mathematics #Missing data #Nonparametric statistics #Observational study #Outcome (game theory) #Statistical Methods and Bayesian Inference #Statistical Methods and Inference #Statistics

paper · pdf · doi:10.1093/biomet/asz048

published in Biometrika 106(4), 875-888 (Oxford University Press)

openalex publication_date 2019/08/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/26

Abstract

Summary It is important to draw causal inference from observational studies, but this becomes challenging if the confounders have missing values. Generally, causal effects are not identifiable if the confounders are missing not at random. In this article we propose a novel framework for nonparametric identification of causal effects with confounders subject to an outcome-independent missingness, which means that the missing data mechanism is independent of the outcome, given the treatment and possibly missing confounders. We then propose a nonparametric two-stage least squares estimator and a parametric estimator for causal effects.

Citations

• The central role of the propensity score in observational studies for causal effects

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