Wang, X;
Du, Y;
Zhu, S;
Ke, L;
Chen, Z;
Hao, J;
Wang, J;
(2021)
Ordering-Based Causal Discovery with Reinforcement Learning.
In:
Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence (IJCAI-21).
(pp. pp. 3566-3573).
IJCAI International Joint Conferences on Artificial Intelligence Organization: Montreal, QC, Canada.
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Abstract
It is a long-standing question to discover causal relations among a set of variables in many empirical sciences. Recently, Reinforcement Learning (RL) has achieved promising results in causal discovery from observational data. However, searching the space of directed graphs and enforcing acyclicity by implicit penalties tend to be inefficient and restrict the existing RL-based method to small scale problems. In this work, we propose a novel RL-based approach for causal discovery, by incorporating RL into the ordering-based paradigm. Specifically, we formulate the ordering search problem as a multi-step Markov decision process, implement the ordering generating process with an encoder-decoder architecture, and finally use RL to optimize the proposed model based on the reward mechanisms designed for each ordering. A generated ordering would then be processed using variable selection to obtain the final causal graph. We analyze the consistency and computational complexity of the proposed method, and empirically show that a pretrained model can be exploited to accelerate training. Experimental results on both synthetic and real data sets shows that the proposed method achieves a much improved performance over existing RL-based method.
| Type: | Proceedings paper |
|---|---|
| Title: | Ordering-Based Causal Discovery with Reinforcement Learning |
| Event: | Thirtieth International Joint Conference on Artificial Intelligence (IJCAI-21) |
| Dates: | 19 Aug 2021 - 27 Aug 2021 |
| ISBN-13: | 9780999241196 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.24963/ijcai.2021/491 |
| Publisher version: | https://doi.org/10.24963/ijcai.2021/491 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Machine Learning Applications: Applications of Reinforcement Learning, Uncertainty in AI: Bayesian Networks |
| UCL classification: | UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science UCL > Provost and Vice Provost Offices > UCL BEAMS UCL |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10146143 |
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