Estimating Causal Effects for Binary Outcomes Using Per-Decision Inverse Probability Weighting
用逐决策逆概率加权估计二元结局因果效应AI 解读 ↓
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用逐决策逆概率加权估计二元结局因果效应
- 研究问题
- 微随机试验的结局窗口内可能发生多次后续处理，如何减少多重逆概率权重导致的方差，精确估计因果偏离效应？
- 方法与思路
- 作者提出两个逐决策 IPW 估计量，第二个加入半参数投影；适用于能写成各子区间子结局最大值的二元结局。
- 材料陈述的贡献
- 摘要称已建立一致性和渐近正态性，并通过模拟及真实应用展示相对既有估计量的效率提升，服务微随机试验分析。
- 阅读关注点
- 应核对因果偏离效应的定义、子结局最大值结构、随机化与权重构造，以及投影改善效率所需的条件。
- 阅读边界
- 这是预印本，同行评审或期刊接收未核实。适用范围受二元结局结构限制；摘要未给出效率提升数值或完整条件。
原文摘要
Micro-randomized trials are commonly conducted for optimizing mobile health interventions such as push notifications for behavior change. In analyzing such trials, causal excursion effects are often of primary interest, and their estimation typically involves inverse probability weighting (IPW). However, in a micro-randomized trial, additional treatments can often occur during the time window over which an outcome is defined, and this can greatly inflate the variance of the causal effect estimator because IPW would involve a product of numerous weights. To reduce variance and improve estimation efficiency, we propose two new estimators using a modified version of IPW, which we call "per-decision IPW". The second estimator further improves efficiency using the projection idea from the semiparametric efficiency theory. These estimators are applicable when the outcome is binary and can be expressed as the maximum of a series of sub-outcomes defined over sub-intervals of time. We establish the estimators' consistency and asymptotic normality. Through simulation studies and real data applications, we demonstrate substantial efficiency improvement of the proposed estimator over existing estimators. The new estimators can be used to improve the precision of primary and secondary analyses for micro-randomized trials with binary outcomes.