外部有效性：从Do演算到总体间的可迁移性
External Validity: From Do-Calculus to Transportability Across Populations
摘要：
经验发现对新环境、新环境或新人群的普遍适用性，通常被称为“外部有效性”，在大多数科学探索中至关重要。本文讨论了一个特殊的问题的概括性，称为“可移植性”，定义为许可证转移因果关系的影响，在实验研究中学习到一个新的人口，其中只能进行观察性研究。我们引入了一个正式的表示，称为“选择图”的利益群体之间的差异和共性表示知识，并使用这种表示，我们减少了问题的可移植性的符号推导的做演算。这种减少产生基于图形的程序，用于在观察任何数据之前决定目标人群中的因果效应是否可以从研究人群中的实验结果中推断出来。当答案是肯定的，程序确定需要从两个人群中获得什么样的实验和观察结果，以及如何将它们结合起来以确保无偏差的传输。
英文摘要：
The generalizability of empirical findings to new environments, settings or populations, often called "external validity," is essential in most scientific explorations. This paper treats a particular problem of generalizability, called "transportability," defined as a license to transfer causal effects learned in experimental studies to a new population, in which only observational studies can be conducted. We introduce a formal representation called "selection diagrams" for expressing knowledge about differences and commonalities between populations of interest and, using this representation, we reduce questions of transportability to symbolic derivations in the do-calculus. This reduction yields graph-based procedures for deciding, prior to observing any data, whether causal effects in the target population can be inferred from experimental findings in the study population. When the answer is affirmative, the procedures identify what experimental and observational findings need be obtained from the two populations, and how they can be combined to ensure bias-free transport.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
