Causal 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 demonstration notebook.