Testing Effect Homogeneity and Confounding in High-Dimensional Experimental and Observational Studies
高维实验与观察性研究中的效应同质性及混杂检验AI 解读 ↓
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高维实验与观察性研究中的效应同质性及混杂检验
- 研究问题
- 不同实验与观察性研究的条件平均处理效应是否一致？这种比较如何帮助评估未观测异质性、潜在混杂及局部效应向总体外推的可能性？
- 方法与思路
- 作者比较跨研究的条件平均处理效应，提出容纳高维协变量的双重机器学习检验，并扩展至工具变量和双重差分，辅以模拟及卒中试验应用。
- 材料陈述的贡献
- 摘要称，该框架可用于研究效应同质性、混杂与可推广性，并在局部识别场景中评估外推问题；文章提供高维检验及有限样本展示。
- 阅读关注点
- 应核对跨研究人群的可比性、同质性与无混杂之间的逻辑条件、局部效应的目标人群，以及检验功效和不拒绝原假设的解释。
- 阅读边界
- 所给来源为 arXiv 预印本，同行评审或期刊接收未获核实；摘要未说明完整比较条件，检验未拒绝效应一致不能直接证明不存在混杂。
原文摘要
We propose a framework for testing the homogeneity of conditional average treatment effects (CATEs) across multiple experimental and observational studies. Our approach leverages multiple randomized trials to assess whether treatment effects vary with unobserved heterogeneity that differs across trials: if CATEs are homogeneous, this indicates the absence of interactions between treatment and unobservables in the mean effect. Comparing CATEs between experimental and observational data further allows evaluation of potential confounding: if the estimands coincide, there is no unobserved confounding; if they differ, deviations may arise from unobserved confounding, effect heterogeneity, or both. We extend the framework to settings with alternative identification strategies, namely instrumental variable settings and panel data with parallel trends assumptions based on differences in differences, where effects are identified only locally for subpopulations such as compliers or treated units. In these contexts, testing homogeneity is useful for assessing whether local effects can be extrapolated to the total population. We suggest a test based on double machine learning that accommodates high-dimensional covariates in a data-driven way and investigate its finite-sample performance through a simulation study. Finally, we apply the test to the International Stroke Trial (IST), a large multi-country randomized controlled trial in patients with acute ischaemic stroke that evaluated whether early treatment with aspirin altered subsequent clinical outcomes. Our methodology provides a flexible tool for both validating identification assumptions and understanding the generalizability of estimated treatment effects.