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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.
  
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― Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction
  ― Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction
        “
Fighting for the acceptance of Bayesian networks in AI was a picnic compared with the fight I had to wage for causal diagrams [in the stormy waters of statistics].
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― The Book of Why: The New Science of Cause and Effect
  ― The Book of Why: The New Science of Cause and Effect

 
    














