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 introduces the front door and back door criteria, to take into account covariates that can affect causal inference. The chapter concludes with two examples of applications of causal models: characterizing patterns of unfairness and accelerating reinforcement learning. Causal discovery is covered in the next chapter.
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Notes
- 1.
This example is based on the information in https://deepmind.com/blog/article/Causal_Bayesian_Networks.
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Sucar, L.E. (2021). Graphical Causal Models. In: Probabilistic Graphical Models. Advances in Computer Vision and Pattern Recognition. Springer, Cham. https://doi.org/10.1007/978-3-030-61943-5_14
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DOI: https://doi.org/10.1007/978-3-030-61943-5_14
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