Semiparametric Estimation with Data Missing Not at Random Using an Instrumental Variable.

ABSTRACT: Missing data occur frequently in empirical studies in health and social sciences, often compromising our ability to make accurate inferences. An outcome is said to be missing not at random (MNAR) if, conditional on the observed variables, the missing data mechanism still depends on the unobserved outcome. In such settings, identification is generally not possible without imposing additional assumptions. Identification is sometimes possible, however, if an instrumental variable (IV) is observed for all subjects which satisfies the exclusion restriction that the IV affects the missingness process without directly influencing the outcome. In this paper, we provide necessary and sufficient conditions for nonparametric identification of the full data distribution under MNAR with the aid of an I

SUBMITTER: Sun B 

PROVIDER: S-EPMC7743916 | biostudies-literature | 2018 Oct

REPOSITORIES: biostudies-literature