Estimation of local treatment effects under the binary instrumental variable model

• 成果类型: Article
• 署名作者: Wang, Linbo; Zhang, Yuexia; Richardson, Thomas S.; Robins, James M.
• 署名单位: University of Toronto; University of Toronto; University of Washington; University of Washington Seattle; Harvard University; Harvard T.H. Chan School of Public Health
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/asab003
• 发表日期: 2021
• 页码: 881894
• 关键词: robust estimation; Causal Inference; identification

摘要

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.