Doubly-robust and heteroscedasticity-aware sample trimming for causal inference
因果推断中兼顾双重稳健与异方差的样本修剪AI 解读 ↓
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因果推断中兼顾双重稳健与异方差的样本修剪
- 研究问题
- 在异方差数据和非参数模型下，如何选择应修剪的观测以降低因果效应估计方差，并对修剪后的不同子总体开展有效推断？
- 方法与思路
- 作者结合倾向得分与条件方差制定修剪规则，建立允许回归估计以四次方根速率收敛的推断理论，并用自助法构造多个子总体的同时置信区间。
- 材料陈述的贡献
- 摘要称，新理论覆盖所提规则及传统倾向得分修剪，无需回归达到参数速率；同时区间支持考察样本量与方差的权衡，并在模拟和数据应用中验证。
- 阅读关注点
- 应核对修剪后目标参数的定义、数据驱动阈值的影响、双重稳健和速率要求，以及多个子总体同时区间的有效性条件与选择后解释。
- 阅读边界
- 这是 arXiv 预印本，不能据此认定已同行评审或获期刊接收；摘要未列出完整正则条件和数值结果，修剪会改变目标总体，不能仅凭方差下降断定整体改进。
原文摘要
A popular method for variance reduction in observational 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 (Yang and Ding, 2018; Crump et al. 2009), but in modern settings where heteroscedastic data are analyzed with non-parametric 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 work 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 sub-populations, 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 semi-synthetic Medicare dataset and find promising results in all settings.