Causal Inference with Confounders Missing Not at Random

• Activity Number: 562 - Advances in Nonparametric Methods in Causal Inference
• Type: Topic Contributed
• Date/Time: Wednesday, July 31, 2019 : 2:00 PM to 3:50 PM
• Sponsor: Section on Statistics in Epidemiology
• Author(s): Linbo Wang* and Shu Yang and Peng Ding
• Companies: University of Toronto and North Carolina State University and University of California, Berkeley
• Keywords: Completeness; Completeness; Integral equation; Outcome-independent missingness

Abstract

It is important to draw causal inference from observational studies, which, however, becomes challenging if the confounders have missing values. Generally, causal effects are not identifiable if the confounders are missing not at random. We propose a novel framework to nonparametrically identify causal effects with confounders subject to an outcome-independent missingness, that is, 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.