Extrapolating causal effects from experiments to novel populations is a common practice in evidence-based-policy, development economics and other social science areas. Drawing on experimental evidence of policy effectiveness, analysts aim to predict the effects of policies in new populations, which might differ importantly from experimental populations. Existing approaches made progress in articulating the sorts of similarities one needs to assume to enable such inferences. It is also recognized, however, that many of these assumptions will remain surrounded by significant uncertainty in practice. Unfortunately, the existing literature says little on how analysts may articulate and manage these uncertainties. This paper aims to make progress on these issues. First, it considers several existing ideas that bear on issues of uncertainty, elaborates the challenges they face, and extracts some useful rationales. Second, it outlines a novel approach, called the support graph approach, that builds on these rationales and allows analysts to articulate and manage uncertainty in extrapolation in a systematic and unified way.

将实验的因果效应外推到新人群是循证政策、发展经济学和其他社会科学领域的常见做法。根据政策有效性的实验证据，分析人员旨在预测政策对新人群的影响，这可能与实验人群有很大不同。现有方法在阐明实现此类推论所需假设的相似性种类方面取得了进展。然而，人们也认识到，这些假设中的许多在实践中仍将存在很大的不确定性。不幸的是，现有文献很少说明分析师如何阐明和管理这些不确定性。本文旨在在这些问题上取得进展。首先，它考虑了几个与不确定性问题有关的现有想法，阐述了他们面临的挑战，并提取了一些有用的理由。其次，它概述了一种新颖的方法，称为支持图形方法，它建立在这些基本原理的基础上，并允许分析师以系统和统一的方式阐明和管理外推中的不确定性。