B1460
Title: Causal inference with confounders missing not at random
Authors: Shu Yang - North Carolina State University (United States) [presenting]
Linbo Wang - University of Toronto (Canada)
Peng Ding - University of California, Berkeley (United States)
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 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.
