Algorithms for Causal Reasoning in Probability Trees
Abstract
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. Here we present concrete algorithms for causal reasoning in discrete probability trees that cover the entire causal hierarchy (association, intervention, and counterfactuals), and operate on arbitrary propositional and causal events. Our work expands the domain of causal reasoning to a very general class of discrete stochastic processes.
Cite
@article{arxiv.2010.12237,
title = {Algorithms for Causal Reasoning in Probability Trees},
author = {Tim Genewein and Tom McGrath and Grégoire Déletang and Vladimir Mikulik and Miljan Martic and Shane Legg and Pedro A. Ortega},
journal= {arXiv preprint arXiv:2010.12237},
year = {2020}
}
Comments
(2nd version with correction to algorithm) 11 pages, 8 figures, 5 algorithms. A companion Colaboratory tutorial is available at https://github.com/deepmind/deepmind-research/tree/master/causal_reasoning