Propensity scores in the design of observational studies for causal effects
倾向得分在观察性因果研究设计中的作用AI 解读 ↓
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倾向得分在观察性因果研究设计中的作用
- 研究问题
- 研究倾向得分如何在查看结果变量之前服务于观察性研究设计，以及协变量平衡如何衔接对未测量混杂的敏感性分析。
- 方法与思路
- 作者以邀请评论回顾倾向得分的发展，讨论匹配与平衡、高维协变量的低维摘要，并介绍增强设计敏感度的比较策略。
- 材料陈述的贡献
- 摘要称可平衡高维协变量的大量低维摘要，并强调研究设计还应寻找偏差证据、使用不同偏差来源的证据及准实验工具。
- 阅读关注点
- 应核对平衡结论的对象与条件、强可忽略性的作用，以及主要未观测协变量的定义和设计敏感度如何指导比较选择。
- 阅读边界
- 这是对早期论文的邀请评论；摘要未给出完整论证或操作细节，观测协变量平衡本身不能排除未测量混杂。
原文摘要
Summary The design of any study, whether experimental or observational, that is intended to estimate the causal effects of a treatment condition relative to a control condition refers to those activities that precede any examination of outcome variables. As defined in our 1983 article (Rosenbaum & Rubin, 1983), the propensity score is the unit-level conditional probability of assignment to treatment versus control given the observed covariates; so the propensity score explicitly does not involve any outcome variables, in contrast to other summaries of variables sometimes used in observational studies. Balancing the distributions of covariates in the treatment and control groups by matching or balancing on the propensity score is therefore an aspect of the design of the observational study. In this invited comment on our 1983 article, we review the situation in the early 1980s and recall some apparent paradoxes that propensity scores helped to resolve. We demonstrate that it is possible to balance an enormous number of low-dimensional summaries of a high-dimensional covariate, even though it is generally impossible to match individuals closely for all the components of a high-dimensional covariate. In a sense, there is only one crucial observed covariate, the propensity score, and there is one crucial unobserved covariate, the principal unobserved covariate. The propensity score and the principal unobserved covariate are equal when treatment assignment is strongly ignorable, that is, unconfounded. Controlling for observed covariates is a prelude to the crucial step from association to causation, the step that addresses potential biases from unmeasured covariates. The design of an observational study also prepares for the step to causation: by selecting comparisons to increase the design sensitivity, by seeking opportunities to detect bias, by seeking mutually supportive evidence affected by different biases, by incorporating quasi-experimental devices such as multiple control groups, and by including the economist’s instruments. All of these considerations reflect the formal development of sensitivity analyses that were largely informal prior to the 1980s.