论文标题
乘以平均和分位数治疗效果的稳健匹配估计值
Multiply robust matching estimators of average and quantile treatment effects
论文作者
论文摘要
倾向得分匹配一直是处理因果推断中混淆的长期传统，但是需要严格的模型假设。在本文中，我们提出了使用两个平衡分数（包括倾向得分和预后得分）的一般因果估计的双分数匹配（DSM）。为了保护可能的模型错误指定，我们为每个分数设置了多个候选模型。我们表明，偏低的DSM估计器实现了多重鲁棒性特性，因为如果倾向得分或预后分数的任何模型正确，则真正因果估计是一致的。
Propensity score matching has been a long-standing tradition for handling confounding in causal inference, however requiring stringent model assumptions. In this article, we propose double score matching(DSM) for general causal estimands utilizing two balancing scores including the propensity score and prognostic score. To gain the protection of possible model misspecification, we posit multiple candidate models for each score. We show that the de-biasing DSM estimator achieves the multiple robustness property in that it is consistent for the true causal estimand if any model of the propensity score or prognostic score is correct.
