Estimation of local treatment effects under the binary instrumental variable model

TL;DR

This paper addresses the estimation of local average treatment effects using binary instrumental variables, highlighting challenges with binary outcomes and proposing novel methods that improve robustness and interpretability.

Contribution

It introduces new modeling and estimation procedures for local average treatment effects under binary IV models, enhancing existing methods in robustness and interpretability.

Findings

Binary outcomes pose greater estimation challenges than continuous ones.

Proposed methods outperform existing approaches in simulations.

Real data analysis demonstrates practical applicability.

Abstract

Instrumental variables are widely used to deal with unmeasured confounding in observational studies and imperfect randomized controlled trials. In these studies, researchers often target the so-called local average treatment effect as it is identifiable under mild conditions. In this paper, we consider estimation of the local average treatment effect under the binary instrumental variable model. We discuss the challenges for causal estimation with a binary outcome, and show that surprisingly, it can be more difficult than the case with a continuous outcome. We propose novel modeling and estimating procedures that improve upon existing proposals in terms of model congeniality, interpretability, robustness or efficiency. Our approach is illustrated via simulation studies and a real data analysis.