Causal Bayesian networks

Staged tree models, probability trees …Causlan Bayesian networks



Some kind of alternative graphical formalism for causal independence graphs 🤷?

discrete probability trees, sometimes also called staged tree models. A probability tree is one of the simplest models for representing the causal generative process of a random experiment or stochastic process The semantics are self-explanatory: each node in the tree corresponds to a potential state of the process, and the arrows indicate both the probabilistic transitions and the causal dependencies between them. Unlike CBNs, probability trees can model context-specific causal dependencies. However, probability trees do not explicitly represent conditional independencies, and thus, when a distribution and its causal relations admit a representation both as a probability tree and a CBN, the latter is more compact.

There is a deepmind demonstaration notebook.

References

Genewein, Tim, Tom McGrath, Grégoire Déletang, Vladimir Mikulik, Miljan Martic, Shane Legg, and Pedro A. Ortega. 2020. “Algorithms for Causal Reasoning in Probability Trees.” arXiv:2010.12237 [cs], October. http://arxiv.org/abs/2010.12237.
Görgen, Christiane. 2017. “An algebraic characterisation of staged trees : their geometry and causal implications.” Ph.D., University of Warwick. http://wrap.warwick.ac.uk/91331/.
Ortega, Pedro A. 2011. “Bayesian Causal Induction.” arXiv:1111.0708 [cs, Stat], November. http://arxiv.org/abs/1111.0708.
———. 2015. “Subjectivity, Bayesianism, and Causality.” Pattern Recognition Letters, Philosophical Aspects of Pattern Recognition, 64 (October): 63–70. https://doi.org/10.1016/j.patrec.2015.04.018.
Shafer, Glenn. 1996. The Art of Causal Conjecture. MIT Press.

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