Identifiability of the instrumental variable model with the treatment and outcome missing not at random

TL;DR

This paper investigates the conditions under which the local average treatment effect remains identifiable in instrumental variable models despite treatment and outcome missing not at random, unifying previous results.

Contribution

It provides new theoretical insights and unifies existing findings on the identifiability of causal effects with non-random missing data in instrumental variable models.

Findings

Identifiability can be achieved under certain assumptions even with non-random missingness.

The paper reviews and consolidates previous results on this topic.

New theoretical conditions for identification are proposed.

Abstract

The instrumental variable model of Imbens and Angrist (1994) and Angrist et al. (1996) allow for the identification of the local average treatment effect, also known as the complier average causal effect. However, many empirical studies are challenged by the missingness in the treatment and outcome. Generally, the complier average causal effect is not identifiable without further assumptions when the treatment and outcome are missing not at random. We study its identifiability even when the treatment and outcome are missing not at random. We review the existing results and provide new findings to unify the identification analysis in the literature.