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.

Genewein, Tim, Tom McGrath, Grégoire Déletang, Vladimir Mikulik, Miljan Martic, Shane Legg, and Pedro A. Ortega. 2020. “Algorithms for Causal Reasoning in Probability Trees.” October 23, 2020.
Görgen, Christiane. 2017. “An Algebraic Characterisation of Staged Trees : Their Geometry and Causal Implications.” Ph.D., University of Warwick.
Ortega, Pedro A. 2011. “Bayesian Causal Induction.” November 29, 2011.
———. 2015. “Subjectivity, Bayesianism, and Causality.” Pattern Recognition Letters, Philosophical Aspects of Pattern Recognition, 64 (October): 63–70.
Shafer, Glenn. 1996. The Art of Causal Conjecture. MIT Press.