Experimental designs for identifying causal mechanisms
Experimental designs for identifying causal mechanisms
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DOI：
10.1111/j.1467-985x.2012.01032.x
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发表时间：
2013-01-01
影响因子：
2
通讯作者：
Yamamoto, Teppei
中科院分区：
文献类型：
--
作者：
Imai, Kosuke;Tingley, Dustin;Yamamoto, Teppei
实验是一种强大的方法论，使科学家能够凭经验建立因果关系。然而，一个重要的批评是，实验仅仅提供了因果关系的黑箱视图，未能识别因果机制。具体而言，批评者认为，尽管实验可以识别平均因果效应，但它们无法解释这种效应产生的过程。如果属实，这代表了实验的严重局限性，特别是对于致力于确定因果机制的社会和医学科学研究而言。我们考虑了几种有助于确定平均自然间接效应的实验设计。其中一些设计需要对中间变量进行完美的操纵，而另一些设计甚至在只能进行不完美操纵的情况下也可以使用。我们使用最近的社会科学实验来说明所提出的每个设计背后的关键思想。
Experimentation is a powerful methodology that enables scientists to establish causal claims empirically. However, one important criticism is that experiments merely provide a black box view of causality and fail to identify causal mechanisms. Specifically, critics argue that, although experiments can identify average causal effects, they cannot explain the process through which such effects come about. If true, this represents a serious limitation of experimentation, especially for social and medical science research that strives to identify causal mechanisms. We consider several experimental designs that help to identify average natural indirect effects. Some of these designs require the perfect manipulation of an intermediate variable, whereas others can be used even when only imperfect manipulation is possible. We use recent social science experiments to illustrate the key ideas that underlie each of the designs proposed.