倾向得分匹配
协变量
观察研究
逆概率加权
混淆
反概率
加权
计量经济学
分位数
统计
平均处理效果
估计
因果推理
边际结构模型
计算机科学
数学
医学
贝叶斯概率
后验概率
经济
放射科
管理
作者
Jared Lunceford,Marie Davidian
摘要
Estimation of treatment effects with causal interpretation from observational data is complicated because exposure to treatment may be confounded with subject characteristics. The propensity score, the probability of treatment exposure conditional on covariates, is the basis for two approaches to adjusting for confounding: methods based on stratification of observations by quantiles of estimated propensity scores and methods based on weighting observations by the inverse of estimated propensity scores. We review popular versions of these approaches and related methods offering improved precision, describe theoretical properties and highlight their implications for practice, and present extensive comparisons of performance that provide guidance for practical use.
科研通智能强力驱动
Strongly Powered by AbleSci AI