Tree Search in DAG Space with Model-based Reinforcement Learning for Causal Discovery

Victor-Alexandru Darvariu, Stephen Hailes, Mirco Musolesi
10/20/2023
cs.LGcs.AI

No official code found. Best implementation: VictorDarvariu/causal-discovery-mbrl (7 stars, Apache-2.0, last commit 2025-04-28).

Abstract

Identifying causal structure is central to many fields ranging from strategic decision-making to biology and economics. In this work, we propose CD-UCT, a model-based reinforcement learning method for causal discovery based on tree search that builds directed acyclic graphs incrementally. We also formalize and prove the correctness of an efficient algorithm for excluding edges that would introduce cycles, which enables deeper discrete search and sampling in DAG space. The proposed method can be applied broadly to causal Bayesian networks with both discrete and continuous random variables. We conduct a comprehensive evaluation on synthetic and real-world datasets, showing that CD-UCT substantially outperforms the state-of-the-art model-free reinforcement learning technique and greedy search, constituting a promising advancement for combinatorial methods.

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Code Implementations(1)

VictorDarvariu/causal-discovery-mbrlCommunity69%
Apache-2.0

Code and data for the article "Tree search in DAG space with model-based reinforcement learning for causal discovery", V.-A. Darvariu et al., Proceedings of the Royal Society A 481(2312): 20240450, 2025.

70R, ShellOct 10, 20241 years agoApache-2.0

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Cite this paper

@article{darvariu2023tree,
  title  = {Tree Search in DAG Space with Model-based Reinforcement Learning for Causal Discovery},
  author = {Victor-Alexandru Darvariu and Stephen Hailes and Mirco Musolesi},
  year   = {2023},
  eprint = {2310.13576},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2310.13576v2}
}

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