A General Algorithm for Deciding Transportability of Experimental Results
A General Algorithm for Deciding Transportability of Experimental Results
复制标题
DOI：
10.1515/jci-2012-0004
复制
发表时间：
2013-05-01
影响因子：
1.4
通讯作者：
Pearl, Judea
中科院分区：
文献类型：
--
作者：
Bareinboim, Elias;Pearl, Judea
在大多数科学探索中，将经验发现推广到新的环境、环境或人群至关重要。本文讨论了一个特殊的普遍性问题，称为“可移植性”，定义为将实验研究中学到的信息转移到不同人群的许可，而只能对其进行观察性研究。鉴于一系列有关两个群体之间的共性和差异的假设，Pearl 和 Bareinboim [1] 得出了允许这种转移发生的充分条件。本文总结了他们的发现，并补充了一个有效的程序来决定何时以及如何可运输性是可行的。它建立了一个必要且充分的条件，用于决定何时可以根据可用的统计信息和实验传递的因果信息来估计目标人群中的因果效应。文章进一步提供了计算传输公式的完整算法，即一种结合观测和实验信息来合成所需因果关系的无偏差估计的方法。最后，本文探讨了可移植性和其他泛化性变体之间的差异。
Generalizing empirical findings to new environments, settings, or populations is essential in most scientific explorations. This article treats a particular problem of generalizability, called "transportability", defined as a license to transfer information learned in experimental studies to a different population, on which only observational studies can be conducted. Given a set of assumptions concerning commonalities and differences between the two populations, Pearl and Bareinboim [1] derived sufficient conditions that permit such transfer to take place. This article summarizes their findings and supplements them with an effective procedure for deciding when and how transportability is feasible. It establishes a necessary and sufficient condition for deciding when causal effects in the target population are estimable from both the statistical information available and the causal information transferred from the experiments. The article further provides a complete algorithm for computing the transport formula, that is, a way of combining observational and experimental information to synthesize bias-free estimate of the desired causal relation. Finally, the article examines the differences between transportability and other variants of generalizability.