Algorithms for Causal Reasoning in Probability Trees
- URL: http://arxiv.org/abs/2010.12237v2
- Date: Thu, 12 Nov 2020 00:49:06 GMT
- Title: Algorithms for Causal Reasoning in Probability Trees
- Authors: Tim Genewein, Tom McGrath, Gr\'egoire D\'eletang, Vladimir Mikulik,
Miljan Martic, Shane Legg, Pedro A. Ortega
- Abstract summary: We present concrete algorithms for causal reasoning in discrete probability trees.
Our work expands the domain of causal reasoning to a very general class of discrete processes.
- Score: 13.572630988699572
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
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