Effects Among the Affected
受影响者中的效应AI 解读 ↓
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分类依据与纠错
AI 根据所提供的论文材料归类：摘要明确研究序贯处理、条件平均处理效应、数据自适应因果参数及其识别，属于因果推断方向。
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受影响者中的效应
- 研究问题
- 如何刻画早期处理的获益程度与后期处理效应的关系，尤其判断停止干预是否对最初获益较多者伤害更大？
- 方法与思路
- 作者用边际结构工作模型概括后期效应随估计的早期条件平均效应变化，并提出目标最大似然估计及影响曲线推断。
- 材料陈述的贡献
- 摘要提出数据自适应因果参数及序贯随机设计下的识别，并以 HIV 照护试验展示初期获益与停止干预损害的关系。
- 阅读关注点
- 应核对该参数与个体早期响应的区别、机器学习估计如何进入目标参数，以及序贯随机化和推断的具体要求。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未核实；摘要未给出完整识别与推断条件，也不能识别每人的真实获益。
原文摘要
Many interventions are both beneficial to initiate and harmful to stop. Traditionally, to determine whether to deploy that intervention in a time-limited way depends on if, on average, the increase in the benefits of starting it outweigh the increase in the harms of stopping it. We propose a novel causal estimand that provides a more nuanced understanding of the effects of such treatments, particularly, how response to an earlier treatment (e.g., treatment initiation) modifies the effect of a later treatment (e.g., treatment discontinuation), thus learning if there are effects among the (un)affected. Specifically, we consider a marginal structural working model summarizing how the average effect of a later treatment varies as a function of the (estimated) conditional average effect of an earlier treatment. We allow for estimation of this conditional average treatment effect using machine learning, such that the causal estimand is a data-adaptive parameter. We show how a sequentially randomized design can be used to identify this causal estimand, and we describe a targeted maximum likelihood estimator for the resulting statistical estimand, with influence curve-based inference. Throughout, we use the Adaptive Strategies for Preventing and Treating Lapses of Retention in HIV Care trial (NCT02338739) as an illustrative example, showing that discontinuation of conditional cash transfers for HIV care adherence was most harmful among those who most had an increase in benefits from them initially.