Identification and Sensitivity Analysis for Multiple Causal Mechanisms: Revisiting Evidence from Framing Experiments
Identification and Sensitivity Analysis for Multiple Causal Mechanisms: Revisiting Evidence from Framing Experiments
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DOI：
10.1093/pan/mps040
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发表时间：
2013-03-01
影响因子：
5.4
通讯作者：
Yamamoto, Teppei
中科院分区：
文献类型：
--
作者：
Imai, Kosuke;Yamamoto, Teppei
社会科学家经常对测试多种因果机制感兴趣，通过这些机制，治疗会影响结果。一种主要的方法是使用线性结构方程模型，并检查相应路径系数的统计意义。然而，这种方法隐含地假设多个机制在因果关系上彼此独立。在这篇文章中，我们考虑了一组替代假设，当存在多个因果相关的中介时，这些假设足以识别平均因果调解效果。我们开发了一种新的敏感性分析，用于检查经验结果对潜在违反关键识别假设的稳健性。我们将所提出的方法应用于三个政治心理学实验，这些实验考察了媒体框架和公众舆论之间的替代因果路径。我们的分析表明，原始结论的有效性高度依赖于假设的替代因果机制的独立性，这突显了所提出的敏感性分析的重要性。所有建议的方法都可以通过开放源码的R包Mediation实现。
Social scientists are often interested in testing multiple causal mechanisms through which a treatment affects outcomes. A predominant approach has been to use linear structural equation models and examine the statistical significance of the corresponding path coefficients. However, this approach implicitly assumes that the multiple mechanisms are causally independent of one another. In this article, we consider a set of alternative assumptions that are sufficient to identify the average causal mediation effects when multiple, causally related mediators exist. We develop a new sensitivity analysis for examining the robustness of empirical findings to the potential violation of a key identification assumption. We apply the proposed methods to three political psychology experiments, which examine alternative causal pathways between media framing and public opinion. Our analysis reveals that the validity of original conclusions is highly reliant on the assumed independence of alternative causal mechanisms, highlighting the importance of proposed sensitivity analysis. All of the proposed methods can be implemented via an open source R package, mediation.