Causal inference with confounders missing not at random

S Yang, L Wang and P Ding

Biometrika, 2019, vol. 106, issue 4, 875-888

Abstract: SummaryIt 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.

Keywords: Completeness; Identifiability; Ill-posed inverse problem; Integral equation; Outcome-independent missingness; Two-stage least squares estimator (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (6)

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