Estimation of local treatment effects under the binary instrumental variable model

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 of causal estimation with a binary outcome and show that, surprisingly, it can be more difficult than in the case with a continuous outcome. We propose novel modelling and estimation procedures that improve upon existing proposals in terms of model congeniality, interpretability, robustness and efficiency. Our approach is illustrated via simulation studies and a real data analysis.

Automated summary

Instrumental variables are commonly used to address unmeasured confounding in observational studies and imperfect randomized controlled trials. This paper focuses on estimating the local average treatment effect under the binary instrumental variable model, highlighting the challenges of causal estimation with a binary outcome and proposing novel modelling and estimation procedures for improvement.