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“Pearl combines aspects of structural equations models and path diagrams. In this approach, assumptions underlying causal statements are coded as missing links in the path diagrams. Mathematical methods are then used to infer, from these path diagrams, which causal effects can be inferred from the data, and which cannot. Pearl's work is interesting, and many researchers find his arguments that path diagrams are a natural and convenient way to express assumptions about causal structures appealing. In our own work, perhaps influenced by the type of examples arising in social and medical sciences, we have not found this approach to aid drawing of causal inferences, and we do not discuss it further in this text.”

Guido W. Imbens, Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction
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Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction by Guido Imbens
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