Tree Search in DAG Space with Model-based Reinforcement Learning for Causal Discovery
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866911775986810880 |
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| author | Darvariu, Victor-Alexandru Hailes, Stephen Musolesi, Mirco |
| author_facet | Darvariu, Victor-Alexandru Hailes, Stephen Musolesi, Mirco |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_13576 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Tree Search in DAG Space with Model-based Reinforcement Learning for Causal Discovery Darvariu, Victor-Alexandru Hailes, Stephen Musolesi, Mirco Machine Learning Artificial Intelligence 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. |
| title | Tree Search in DAG Space with Model-based Reinforcement Learning for Causal Discovery |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2310.13576 |