Causal Graphical Models with Latent Variables: Learning and Inference

  title={Causal Graphical Models with Latent Variables: Learning and Inference},
  author={Stijn Meganck and Philippe Leray and Bernard Manderick},
Several paradigms exist for modeling causal graphical models for discrete variables that can handle latent variables without explicitly modeling them quantitatively. Applying them to a problem domain consists of different steps: structure learning, parameter learning and using them for probabilistic or causal inference. We discuss two well-known formalisms, namely semi-Markovian causal models and maximal ancestral graphs and indicate their strengths and limitations. Previously an algorithm has… 

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