Department of Statistics, Columbia University, New York, NY 10027, USA.
Neuroimage. 2013 Aug 1;76:446-9. doi: 10.1016/j.neuroimage.2011.11.027. Epub 2011 Nov 17.
Our original comment (Lindquist and Sobel, 2011) made explicit the types of assumptions neuroimaging researchers are making when directed graphical models (DGMs), which include certain types of structural equation models (SEMs), are used to estimate causal effects. When these assumptions, which many researchers are not aware of, are not met, parameters of these models should not be interpreted as effects. Thus it is imperative that neuroimaging researchers interested in issues involving causation, for example, effective connectivity, consider the plausibility of these assumptions for their particular problem before using SEMs. In cases where these additional assumptions are not met, researchers may be able to use other methods and/or design experimental studies where the use of unrealistic assumptions can be avoided. Pearl does not disagree with anything we stated. However, he takes exception to our use of potential outcomes' notation, which is the standard notation used in the statistical literature on causal inference, and his comment is devoted to promoting his alternative conventions. Glymour's comment is based on three claims that he inappropriately attributes to us. Glymour is also more optimistic than us about the potential of using directed graphical models (DGMs) to discover causal relations in neuroimaging research; we briefly address this issue toward the end of our rejoinder.
我们最初的评论（Lindquist 和 Sobel，2011）明确指出了神经影像学研究人员在使用定向图形模型（DGM），包括某些类型的结构方程模型（SEM）时所做出的假设类型，这些模型用于估计因果效应。当这些假设（许多研究人员没有意识到）不成立时，这些模型的参数不应被解释为效应。因此，对于那些对因果关系问题（例如有效连接）感兴趣的神经影像学研究人员来说，在使用 SEM 之前，应考虑其特定问题中这些假设的合理性。在这些额外假设不成立的情况下，研究人员可能能够使用其他方法和/或设计可以避免使用不切实际假设的实验研究。Pearl 并没有不同意我们所说的任何内容。然而，他对我们使用潜在结果表示法提出异议，这是因果推理统计文献中使用的标准表示法，他的评论专门用于推广他的替代约定。Glymour 的评论基于他不恰当地归因于我们的三个主张。Glymour 对使用定向图形模型（DGM）在神经影像学研究中发现因果关系的潜力也比我们更为乐观；我们在回应的最后简要讨论了这个问题。
