Doubly robust and heteroscedasticity-aware sample trimming for causal inference
因果推断中兼顾双重稳健性与异方差的样本修剪AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要明确研究因果效应估计中的倾向概率修剪、异方差与修剪后推断。
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因果推断中兼顾双重稳健性与异方差的样本修剪
- 研究问题
- 在异方差数据和非参数估计下，如何选择需要修剪的样本，并对修剪后子总体的因果效应进行有效推断？
- 方法与思路
- 作者同时利用倾向概率与条件方差选择修剪对象，建立允许干扰回归满足四次根速率要求的推断理论。
- 材料陈述的贡献
- 摘要称理论也适用于常规倾向概率修剪，并提出对多个修剪子总体构造同时有效置信区间的 bootstrap 方法。
- 阅读关注点
- 应核对修剪规则、目标子总体与因果估计量的定义、速率条件，以及数据驱动修剪和同时推断的处理方式。
- 阅读边界
- 仅据所给 Crossref 摘要，无法确定完整识别及正则条件；修剪会改变目标总体，模拟和应用表现未给出具体数值。
原文摘要
Summary A popular method for variance reduction in causal inference is propensity-based trimming, the practice of removing units with extreme propensities from the sample. This practice has theoretical grounding when the data are homoscedastic and the propensity model is parametric (Crump et al., 2009; Yang & Ding, 2018), but in modern settings where heteroscedastic data are analysed with nonparametric models, existing theory fails to support current practice. In this work, we address this challenge by developing new methods and theory for sample trimming. Our contributions are three-fold. First, we describe novel procedures for selecting which units to trim. Our procedures differ from previous works in that we trim, not only units with small propensities, but also units with extreme conditional variances. Second, we give new theoretical guarantees for inference after trimming. In particular, we show how to perform inference on the trimmed subpopulation without requiring that our regressions converge at parametric rates. Instead, we make only fourth-root rate assumptions like those in the double machine learning literature. This result applies to conventional propensity-based trimming as well, and thus may be of independent interest. Finally, we propose a bootstrap-based method for constructing simultaneously valid confidence intervals for multiple trimmed subpopulations, which are valuable for navigating the trade-off between sample size and variance reduction inherent in trimming. We validate our methods in simulation, on the 2007–2008 National Health and Nutrition Examination Survey and on a semisynthetic Medicare dataset, and find promising results in all settings.