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Miljan Martic
23 lines
1.3 KiB
Markdown
23 lines
1.3 KiB
Markdown
# Algorithms for Causal Reasoning in Probability Trees
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*By the AGI Safety Analysis Team @ DeepMind*
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Probability trees are one of the simplest models of causal generative processes.
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They possess clean semantics and are strictly more general than causal Bayesian
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networks, as they are able to e.g. represent causal relations that causal Bayesian
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networks cannot. Yet, they have received little attention from the AI and ML
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community. Here we present new algorithms for causal reasoning in discrete
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probability trees that cover the entire causal hierarchy (association, intervention,
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and counterfactuals), and operate on arbitrary propositional and causal events. Our
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work expands the domain of causal reasoning to a very general class of discrete
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stochastic processes.
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For details, see our paper [Algorithms for Causal Reasoning in Probability Trees](https://arxiv.org/abs/2010.12237).
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The accompanying colab is available here:
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[](https://colab.research.google.com/github/deepmind/deepmind_research/blob/master/causal_reasoning/Causal_Reasoning_in_Probability_Trees.ipynb)
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If you use the code here please cite this paper.
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> Tim Genewein*, Tom McGrath*, Grégoire Delétang*, Vladimir Mikulik*, Miljan Martic, Shane Legg, Pedro A. Ortega. [\[arXiv\]](https://arxiv.org/abs/2010.12237)
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