In plain words: Probability trees are branching diagrams of how causes lead to effects, and unlike standard causal networks they can show a cause matters only sometimes. New algorithms read them to answer what happened, what if we act, and what would have been, for any event.
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.
Tim Genewein, Tom McGrath, Grégoire Déletang, Vladimir Mikulik, Miljan Martic, Shane Legg, Pedro A. Ortega
arXiv:2010.12237 · cs.AI, cs.LG · submitted Oct 23, 2020 · updated Nov 12, 2020
abstract · pdf · html · (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
