Orthogonal machine learning for conditional odds and risk ratios
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条件优势比与风险比的正交机器学习
- 研究问题
- 如何将处理效应异质性学习中的双重稳健和正交方法，从条件平均效应推广到条件优势比与条件风险比？
- 方法与思路
- 作者推广 DR-learner 和 R-learner，推导优势比与风险比的正交风险函数及伪结局，并开展多种分布下的模拟比较。
- 材料陈述的贡献
- 摘要称，伪结局具有二阶条件均值余项性质，复杂模拟中非参数方法降低偏差和均方误差，并展示健康调查应用。
- 阅读关注点
- 应核对条件优势比与风险比的目标定义、正交化推导、概率接近边界时的条件，以及模拟比较和决策规则评价设计。
- 阅读边界
- 所据为 arXiv 预印本，同行评审或期刊接收未核实；摘要未列出完整识别条件，模拟优势与决策改善不能普遍化。
原文摘要
Conditional effects are commonly used measures for understanding how treatment effects vary across different groups, and are often used to target treatments/interventions to groups who benefit most. In this work we review existing methods and propose novel ones, focusing on the odds ratio (OR) and the risk ratio (RR). While estimation of the conditional average treatment effect (ATE) has been widely studied, estimators for the OR and RR lag behind, and cutting edge estimators such as those based on doubly robust transformations or orthogonal risk functions have not been generalized to these parameters. We propose such a generalization here, focusing on the DR-learner and the R-learner. We derive orthogonal risk functions for the OR and RR and show that the associated pseudo-outcomes satisfy second-order conditional-mean remainder properties analogous to the ATE case. We also evaluate estimators for the conditional ATE, OR, and RR in a comprehensive nonparametric Monte Carlo simulation study to compare them with common alternatives under hundreds of different data-generating distributions. Our numerical studies provide empirical guidance for choosing an estimator. For instance, they show that while parametric models are useful in very simple settings, the proposed nonparametric estimators significantly reduce bias and mean squared error in the more complex settings expected in the real world. We illustrate the methods in the analysis of physical activity and sleep trouble in U.S. adults using data from the National Health and Nutrition Examination Survey (NHANES). The results demonstrate that our estimators uncover substantial treatment effect heterogeneity that is obscured by traditional regression approaches and lead to improved treatment decision rules, highlighting the importance of data-adaptive methods for advancing precision health research.