倾向得分匹配
观察研究
因果推理
混淆
推论
计算机科学
计量经济学
医学
统计
人工智能
数学
作者
Benjamin Y. Andrew,M. Alan Brookhart,Rupert M. Pearse,Karthik Raghunathan,Vijay Krishnamoorthy
标识
DOI:10.1016/j.bja.2023.06.054
摘要
Causal inference in observational research requires a careful approach to adjustment for confounding. One such approach is the use of propensity score analyses. In this editorial, we focus on the role of propensity score-based methods in estimating causal effects from non-randomised observational data. We highlight the details, assumptions, and limitations of these methods and provide authors with guidelines for the conduct and reporting of propensity score analyses.
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