Abstract
This chapter discusses causal graphical models for discrete variables that can handle latent variables without explicitly modeling them quantitatively. In the uncertainty in artificial intelligence area there exist several paradigms for such problem domains. Two of them are semi-Markovian causal models and maximal ancestral graphs. Applying these techniques to a problem domain consists of several steps, typically: structure learning from observational and experimental data, parameter learning, probabilistic inference, and, quantitative causal inference.
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Leray, P., Meganek, S., Maes, S., Manderick, B. (2008). Causal Graphical Models with Latent Variables: Learning and Inference. In: Holmes, D.E., Jain, L.C. (eds) Innovations in Bayesian Networks. Studies in Computational Intelligence, vol 156. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85066-3_9
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