Identification and estimation of the conditional average treatment effect with nonignorable missing covariates, treatment, and outcome

TL;DR

This paper develops methods to identify and estimate the conditional average treatment effect in observational studies with multivariate missing not at random data, providing tools for bias correction and robustness analysis.

Contribution

It establishes nonparametric identification of CATE under complex MNAR mechanisms and introduces estimators and sensitivity analysis frameworks.

Findings

Nonparametric identification of CATE under MNAR.

Development of estimators for partially observed data.

Framework for sensitivity analysis of missingness assumptions.

Abstract

Treatment effect heterogeneity is central to policy evaluation, social science, and precision medicine, where interventions can affect individuals differently. In observational studies, covariates, treatment, and outcomes are often only partially observed. When missingness depends on unobserved values (missing not at random; MNAR), standard methods can yield biased estimates of the conditional average treatment effect (CATE). This paper establishes nonparametric identification of the CATE under multivariate MNAR mechanisms that allow covariates, treatment, and outcomes to be MNAR. It also develops nonparametric and parametric estimators and proposes a sensitivity analysis framework for assessing robustness to violations of the missingness assumptions.