Estimation of local treatment effects under the binary instrumental variable model

Bootstrap tests for distributional treatment effects in instrumental variable models

Linbo Wang, Yuexia Zhang, Thomas S Richardson and James M Robins

Biometrika, 2021, vol. 108, issue 4, 881-894

Abstract: SummaryInstrumental 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.

Keywords: Causal inference; Model compatibility; Semiparametric efficiency; Variation independence (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:oup:biomet:v:108:y:2021:i:4:p:881-894.

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