A Semiparametric Instrumental Variable Approach to Optimal Treatment Regimes Under Endogeneity

• 成果类型: Article
• 署名作者: Cui, Yifan; Tchetgen Tchetgen, Eric
• 署名单位: University of Pennsylvania
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2020.1783272
• 发表日期: 2020
• 页码: 162-173
• 关键词: estimating individualized treatment; Dynamic Treatment Regimes; Robust Estimation; Causal Inference; treatment rules; learning-methods; models; identification

摘要

There is a fast-growing literature on estimating optimal treatment regimes based on randomized trials or observational studies under a key identifying condition of no unmeasured confounding. Because confounding by unmeasured factors cannot generally be ruled out with certainty in observational studies or randomized trials subject to noncompliance, we propose a general instrumental variable (IV) approach to learning optimal treatment regimes under endogeneity. Specifically, we establish identification of both value functionfor a given regimeand optimal regimeswith the aid of a binary IV, when no unmeasured confounding fails to hold. We also construct novel multiply robust classification-based estimators. Furthermore, we propose to identify and estimate optimal treatment regimes among those who would comply to the assigned treatment under a monotonicity assumption. In this latter case, we establish the somewhat surprising result that complier optimal regimes can be consistently estimated without directly collecting compliance information and therefore without the complier average treatment effect itself being identified. Our approach is illustrated via extensive simulation studies and a data application on the effect of child rearing on labor participation.for this article are available online.