Optimal Covariate Adjustment beyond the Average Treatment Effect: Treated-Population and Overlap-Weighted Estimands
超越平均处理效应的最优协变量调整：已处理人群与重叠加权目标AI 解读 ↓
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超越平均处理效应的最优协变量调整：已处理人群与重叠加权目标
- 研究问题
- 平均处理效应的图形最优调整结论，能否推广到已处理人群及重叠加权目标，增加协变量又会怎样改变效率界？
- 方法与思路
- 作者推导有效调整集扩展时效率界变化的恒等式，以同一因果图上的两个分布构造反例，并推广到倾向得分依赖的加权目标。
- 材料陈述的贡献
- 摘要称，已处理人群目标不存在对所有相容分布统一最优的图形准则；结果还揭示效率反转、特定条件下的最优性及目标漂移。
- 阅读关注点
- 应核对有效调整集的定义、效率界比较与无效应修饰条件，并区分增加协变量造成的估计效率变化和加权目标本身的变化。
- 阅读边界
- 本资料为 arXiv 预印本，同行评审及期刊接收未核实；摘要未给出完整正则条件，模拟与 LaLonde 数据仅被列为说明。
原文摘要
Graphical causal inference supplies a complete theory of efficient covariate adjustment for the average treatment effect: one adjustment set, computable from the graph, is optimal under every compatible distribution. We show that this is a property of the average treatment effect's inverse-prevalence weights, not of causal estimands in general. For the average treatment effect on the treated we index the efficiency bound by the adjustment set and derive exact identities for its change under treatment-side and outcome-side extensions of a valid set. Covariates that predict only the treated-arm outcome are exactly efficiency-neutral, and covariates that predict the control-arm outcome can strictly increase the bound when the propensity is below one half -- a reversal of the supplementation lemma whose source is an arithmetic-geometric-mean inequality that holds for the average treatment effect and fails for the treated-population estimand. A construction with two faithful distributions on one graph proves that no graphical optimality criterion exists for the treated-population estimand; under no effect modification the ATE-optimal set is nonetheless optimal among the graphically valid sets, with an exact expression for its advantage. The results extend to weighted average treatment effects with propensity-dependent weights, yielding symmetric thresholds for overlap weights, an estimand-drift phenomenon under instrument adjustment, and a characterization of constant weights as the only smooth positive weights for which outcome-side supplementation never increases the bound. Simulations and the LaLonde data provide illustrations.