提出模拟方法评估个体化干预中未测混杂因素的影响。
Simulation-Based Sensitivity Analysis in Optimal Treatment Regimes and Causal Decomposition with Individualized Interventions
- 通过模拟未测混杂变量，分析个体化治疗方案的偏差
- 建立二元风险因素未测混杂强度的量化基准
- 在高中纵向研究数据中验证方法有效性
因果分解分析旨在评估调整风险因素对降低结果差异的社会不平等的影响。近年来，该分析结合个体特征，利用最优治疗方案（OTRs）来调整风险因素。由于新定义的个体化效应依赖于无遗漏混杂假设，因此开发考虑潜在遗漏混杂的敏感性分析至关重要。此外，当前的OTRs和个体化效应主要基于二元风险因素，尚无正式方法利用可观测协变量为二元风险因素的遗漏混杂强度提供基准。为此，我们扩展了一种基于仿真的敏感性分析，通过模拟未测量混杂因子，解决从推导OTRs和估计个体化效应中产生的两类偏差。同时，我们提出一种正式的边界策略，用于衡量二元风险因素的遗漏混杂强度。利用2009年高中纵向研究（HSLS:09）数据，我们展示了该敏感性分析与基准方法的应用。
原文摘要 · Abstract (English)
Causal decomposition analysis aims to assess the effect of modifying risk factors on reducing social disparities in outcomes. Recently, this analysis has incorporated individual characteristics when modifying risk factors by utilizing optimal treatment regimes (OTRs). Since the newly defined individualized effects rely on the no omitted confounding assumption, developing sensitivity analyses to account for potential omitted confounding is essential. Moreover, OTRs and individualized effects are primarily based on binary risk factors, and no formal approach currently exists to benchmark the strength of omitted confounding using observed covariates for binary risk factors. To address this gap, we extend a simulation-based sensitivity analysis that simulates unmeasured confounders, addressing two sources of bias emerging from deriving OTRs and estimating individualized effects. Additionally, we propose a formal bounding strategy that benchmarks the strength of omitted confounding for binary risk factors. Using the High School Longitudinal Study 2009 (HSLS:09), we demonstrate this sensitivity analysis and benchmarking method.
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