Complier Stochastic Direct Effects: Identification and Robust Estimation

• 成果类型: Article
• 署名作者: Rudolph, Kara E.; Sofrygin, Oleg; van der Laan, Mark J.
• 署名单位: Columbia University; University of California System; University of California Berkeley
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2019.1704292
• 发表日期: 2021
• 页码: 1254-1264
• 关键词: mediation analysis; causal; inference

摘要

Mediation analysis is critical to understanding the mechanisms underlying exposure-outcome relationships. In this article, we identify the instrumental variable-direct effect of the exposure on the outcome not through the mediator, using randomization of the instrument. We call this estimand the complier stochastic direct effect (CSDE). To our knowledge, such an estimand has not previously been considered or estimated. We propose and evaluate several estimators for the CSDE: a ratio of inverse-probability of treatment-weighted estimators (IPTW), a ratio of estimating equation estimators (EE), a ratio of targeted minimum loss-based estimators (TMLE), and a TMLE that targets the CSDE directly. These estimators are applicable for a variety of study designs, including randomized encouragement trials, like the Moving to Opportunity housing voucher experiment we consider as an illustrative example, treatment discontinuities, and Mendelian randomization. We found the IPTW estimator to be the most sensitive to finite sample bias, resulting in bias of over 40% even when all models were correctly specified in a sample size of N = 100. In contrast, the EE estimator and TMLE that targets the CSDE directly were far less sensitive. The EE and TML estimators also have advantages in terms of efficiency and reduced reliance on correct parametric model specification. for this article are available online.