Longitudinal weighted and trimmed treatment effects with flip interventions
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基于翻转干预的纵向加权与修剪处理效应
- 研究问题
- 当纵向数据在非基线时点违反正值性时，如何赋予加权和修剪效应可实施的政策含义，并构造可识别目标及灵活估计方法？
- 方法与思路
- 作者定义随机翻转干预，保留原本接受目标处理者的状态，并按权重概率翻转其他人的处理；利用有效影响函数构造稳健估计量。
- 材料陈述的贡献
- 摘要称，该干预连接单时点加权效应与纵向政策目标，可在正值性违反下识别；平滑权重情形的估计量具有相应根号样本量性质。
- 阅读关注点
- 应核对翻转概率、目标政策和纵向效应的定义，检查识别仍需哪些条件，以及平滑权重、修剪指标和稳健估计速率的适用区别。
- 阅读边界
- 这是 arXiv 预印本，同行评审或接收未核实；摘要未展开非参数条件，允许正值性违反不等于无需其他识别假设或能识别原始 ATE。
原文摘要
Weighting and trimming are popular methods for addressing positivity violations in causal inference. While well-studied with single-timepoint data, standard methods do not easily generalize to address non-baseline positivity violations in longitudinal data, and remain vulnerable to such violations. In this paper, we extend weighting and trimming to longitudinal data via stochastic ``flip'' interventions, which maintain the treatment status of subjects who would have received the target treatment, and flip others' treatment to the target with probability equal to their weight (e.g., overlap weight, trimming indicator). We first show, in single-timepoint data, that flip interventions yield a large class of weighted average treatment effects, ascribing a novel policy interpretation to these popular weighted estimands. With longitudinal data, we then show that flip interventions provide interpretable weighting or trimming on non-baseline covariates and, crucially, yield effects that are identifiable under arbitrary positivity violations. Moreover, we demonstrate that flip interventions are policy-relevant since they could be implemented in practice. By contrast, we show that alternative approaches for weighting on non-baseline covariates fail to achieve this property. We derive flexible and efficient estimators based on efficient influence functions when the weight is a smooth function of the propensity score. Namely, we construct multiply robust-style and sequentially doubly robust-style estimators that achieve root-n consistency and asymptotic normality under nonparametric conditions. Finally, we demonstrate our methods through an analysis of the effect of union membership on earnings.