When the ends don't justify the means: Learning a treatment strategy to prevent harmful indirect effects
当结果不足以证明手段合理：学习避免有害间接效应的治疗策略AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要围绕治疗分配、中介效应及治疗后混杂展开，直接属于因果推断。
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当结果不足以证明手段合理：学习避免有害间接效应的治疗策略
- 研究问题
- 当治疗总体上有益，却可能通过中介产生有害影响时，如何识别具有此类风险的个体子群，并据此考虑治疗分配？
- 方法与思路
- 作者结合中介分析与最优治疗规则，提出非参数方法，并纳入可能混杂中介与结局关系的治疗后变量。
- 材料陈述的贡献
- 摘要称方法用于识别有害间接效应子群，并在 Moving to Opportunity 实验中分析平均总效应有益的男孩群体。
- 阅读关注点
- 应核对间接效应的具体定义、治疗后混杂的处理方式，以及预测子群与治疗规则之间如何衔接并进行统计评价。
- 阅读边界
- 这是 arXiv 预印本，不能据此确认同行评审或期刊接收；摘要未说明识别假设、估计细节或个体风险预测的准确程度。
原文摘要
There is a growing literature on finding so-called optimal treatment rules, which are rules by which to assign treatment to individuals based on an individual's characteristics, such that a desired outcome is maximized. A related goal entails identifying individuals who are predicted to have a harmful indirect effect (the effect of treatment on an outcome through mediators) even in the presence of an overall beneficial effect of the treatment on the outcome. In some cases, the likelihood of a harmful indirect effect may outweigh a likely beneficial overall effect, and would be reason to caution against treatment for indicated individuals. We build on both the current mediation and optimal treatment rule literature to propose a method of identifying a subgroup for which the treatment effect through the mediator is harmful. Our approach is nonparametric, incorporates post-treatment variables that may confound the mediator-outcome relationship, and does not make restrictions on the distribution of baseline covariates, mediating variables (considered jointly), or outcomes. We apply the proposed approach to identify a subgroup of boys in the Moving to Opportunity housing voucher experiment who are predicted to have harmful indirect effects, though the average total effect is beneficial.