Overlap in observational studies with high-dimensional covariates
高维协变量观察性研究中的重叠性AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要明确讨论观察性因果效应估计所需的无混杂性、重叠性及其高维限制。
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高维协变量观察性研究中的重叠性
- 研究问题
- 观察性因果研究增加协变量以支持无混杂性时，严格重叠性会对处理组与对照组的协变量分布施加多强的限制？
- 方法与思路
- 作者运用信息论结果，将严格重叠与两组协变量分布的整体差异联系起来，推导平均均值不平衡的界并讨论稀疏与修剪。
- 材料陈述的贡献
- 摘要称这些显式界随维度增加而更加严格，从而形式化高维重叠的维数灾难，并揭示常见因果分析假设之间的张力。
- 阅读关注点
- 应核对严格重叠的定量定义、均值不平衡的尺度与界的条件，以及稀疏性和修剪如何改变目标总体及相关限制。
- 阅读边界
- 仅据所给出版社摘要，无法判断界的精确形式、紧致性或实际诊断办法；这些结果也不能单独验证应用中的无混杂性。
原文摘要
Estimating causal effects under exogeneity hinges on two key assumptions: unconfoundedness and overlap. Researchers often argue that unconfoundedness is more plausible when more covariates are included in the analysis. Less discussed is the fact that covariate overlap is more difficult to satisfy in this setting. In this paper, we explore the implications of overlap in observational studies with high-dimensional covariates and formalize curse-of-dimensionality argument, suggesting that these assumptions are stronger than investigators likely realize. Our key innovation is to explore how strict overlap restricts global discrepancies between the covariate distributions in the treated and control populations. Exploiting results from information theory, we derive explicit bounds on the average imbalance in covariate means under strict overlap and show that these bounds become more restrictive as the dimension grows large. We discuss how these implications interact with assumptions and procedures commonly deployed in observational causal inference, including sparsity and trimming.