Causal Inference Engine based in the paper[1] and implemented in Koltin using Probability Trees.

According the paper, probability trees are one of the simplest models of causal generative processes. They possess clean semantics and -- unlike causal Bayesian networks -- they can represent context-specific causal dependencies, which are necessary for e.g. causal induction. Yet, they have received little attention from the AI and ML community.

[1] Algorithms for Causal Reasoning in Probability Trees

Genewein et al. (2020)

DeepMind

https://arxiv.org/abs/2010.12237