Causal Inference With Outcome-Dependent Missingness And Self-Censoring.

ABSTRACT: We consider missingness in the context of causal inference when the outcome of interest may be missing. If the outcome directly affects its own missingness status, i.e., it is "self-censoring", this may lead to severely biased causal effect estimates. Miao et al. [2015] proposed the shadow variable method to correct for bias due to self-censoring; however, verifying the required model assumptions can be difficult. Here, we propose a test based on a randomized incentive variable offered to encourage reporting of the outcome that can be used to verify identification assumptions that are sufficient to correct for both self-censoring and confounding bias. Concretely, the test confirms whether a given set of pre-treatment covariates is sufficient to block all backdoor paths between the treatmen

SUBMITTER: Chen JM 

PROVIDER: S-EPMC11905187 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature