论文标题
重新审视倾向得分的核心角色：在因果机器学习时代弥合平衡和效率
Revisiting the propensity score's central role: Towards bridging balance and efficiency in the era of causal machine learning
论文作者
论文摘要
大约四十年前，Rosenbaum＆Rubin（1983）在现在的灵感贡献中引入了倾向评分作为观察性研究环境中绘制因果推断的中心量的批判性表征。从那以后的几十年中，在因果推断的几个研究方面取得了很多进展，特别是包括重新加权和匹配范式。专注于前者，特别是其与机器学习和半参数效率理论的交集，我们重新检查了倾向得分在现代方法论发展中的作用。正如Rosenbaum＆Rubin（1983）的贡献促使人们专注于倾向得分的平衡特性时，我们重新检查了该特性在因果效应的渐近效率估计值的发展中发挥作用的程度以及如何发挥作用。此外，我们讨论了平衡属性与分数方程式形式的有效估计之间的联系，并提出了评估估计器是否达到平衡的分数测试。
About forty years ago, in a now--seminal contribution, Rosenbaum & Rubin (1983) introduced a critical characterization of the propensity score as a central quantity for drawing causal inferences in observational study settings. In the decades since, much progress has been made across several research fronts in causal inference, notably including the re-weighting and matching paradigms. Focusing on the former and specifically on its intersection with machine learning and semiparametric efficiency theory, we re-examine the role of the propensity score in modern methodological developments. As Rosenbaum & Rubin (1983)'s contribution spurred a focus on the balancing property of the propensity score, we re-examine the degree to which and how this property plays a role in the development of asymptotically efficient estimators of causal effects; moreover, we discuss a connection between the balancing property and efficient estimation in the form of score equations and propose a score test for evaluating whether an estimator achieves balance.
