Abstract
This chapter gives an introduction to causal modeling, in particular to causal Bayesian networks. It starts by introducing causal models and their importance. Then causal Bayesian networks are described, including two types of causal reasoning, prediction and counterfactuals. It continues with the topic of learning causal models, presenting one of the state-of-the-art techniques. Finally, it shows an example of learning causal models from real-world data about children with Attention Deficit Hyperactivity Disorder.
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References
Aitken, J.S.: Learning Bayesian networks: approaches and issues. Knowl. Eng. Rev. 26(2), 99–157 (2011)
Claassen, T., Heskes, T: A Bayesian approach to constraint based causal inference. In: Proceedings of Uncertainty in Artificial Intelligence (UAI), AUAI Press, pp. 207–216 (2012)
Pearl, J.: Causality: Models. Reasoning and Inference. Cambridge University Press, New York (2009)
Sokolova, E., Groot, P., Classen, T., Heskes, T.: Causal discovery from databases with discrete and continuous variables. In: Probabilistic graphical models (PGM). Springer, pp. 442–457 (2014)
Spirtes, P., Glymour, C., Scheines, R.: Causation, Prediction, and Search. MIT Press, Cambridge (2000)
Wright, S.: Correlation and causation. J. Agric. Res. 20, 557–585 (1921)
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Sucar, L.E. (2015). Graphical Causal Models. In: Probabilistic Graphical Models. Advances in Computer Vision and Pattern Recognition. Springer, London. https://doi.org/10.1007/978-1-4471-6699-3_13
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DOI: https://doi.org/10.1007/978-1-4471-6699-3_13
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Publisher Name: Springer, London
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Online ISBN: 978-1-4471-6699-3
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