Propensity score weighting across counterfactual worlds: longitudinal effects under positivity violations
跨反事实世界的倾向得分加权：正值性违背下的纵向效应AI 解读 ↓
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跨反事实世界的倾向得分加权：正值性违背下的纵向效应
- 研究问题
- 当纵向处理路径对部分个体不可观察时，能否定义可识别的处理效应，同时区分机制解释、共同支持与干预可实施性？
- 方法与思路
- 作者提出累计跨世界加权效应，沿时间及两条反事实历史调整倾向权重，并将密度比估计改写为回归来构造估计量。
- 材料陈述的贡献
- 摘要称，新目标无需正值性即可识别，但机制解释依赖部分共同支持且对应不可实施干预；另提出具有渐近性质的估计量。
- 阅读关注点
- 应核对跨世界权重与识别条件、部分共同支持的定义，以及目标退化为零的情形和辅助估计量所需收敛率。
- 阅读边界
- 这是arXiv预印本，同行评审及期刊接收未核实；无需正值性不代表无识别假设，所得效应也不能直接解释为可实施政策效果。
原文摘要
When examining a contrast between two interventions, longitudinal causal inference studies frequently encounter positivity violations when one or both regimes are impossible to observe for some subjects. Existing weighting methods either assume positivity holds or produce effects that conflate interventions' impacts on ultimate outcomes with their effects on intermediate treatments and covariates. We propose a novel class of estimands -- cumulative cross-world weighted effects -- that weights potential outcome differences using propensity scores adapting to positivity violations cumulatively across timepoints and simultaneously across both counterfactual treatment histories. This new estimand isolates mechanistic differences between treatment regimes, is identifiable without positivity assumptions, and circumvents the limitations of existing longitudinal methods. Further, our analysis reveals two fundamental insights about longitudinal causal inference under positivity violations. First, while mechanistically meaningful, these effects correspond to non-implementable interventions, exposing a core interpretability-implementability tradeoff. Second, the identified effects faithfully capture mechanistic differences only under a partial common support assumption; violations cause the identified functional to collapse to zero, even when the causal effect is non-zero. We develop doubly robust-style estimators that achieve asymptotic normality and parametric convergence under nonparametric assumptions on the nuisance estimators. To this end, we reformulate challenging density ratio estimation as regression function estimation, which is achievable with standard machine learning methods. We illustrate our methods through analysis of union membership's effect on earnings.