Causal inference with confounders missing not at random

2019/08/19 by S Yang, Shu Yang, L Wang +3 · 9 citations
Mathematics · #Advanced Causal Inference Techniques #Statistical Methods and Inference #Statistical Methods and Bayesian Inference

paper · pdf · doi:10.1093/biomet/asz048

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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