Causal Discovery via Bayesian Optimization

• URL: http://arxiv.org/abs/2501.14997v1
• Date: Sat, 25 Jan 2025 00:19:38 GMT
• Title: Causal Discovery via Bayesian Optimization
• Authors: Bao Duong, Sunil Gupta, Thin Nguyen,
• Abstract summary: We propose DrBO (DAG recovery via Bayesian Optimization) to find high-scoring DAGs. DrBO is computationally efficient and can find the accurate DAG in fewer trials and less time than existing state-of-the-art methods.
• Score: 14.698661810621326
• License:
• Abstract: Existing score-based methods for directed acyclic graph (DAG) learning from observational data struggle to recover the causal graph accurately and sample-efficiently. To overcome this, in this study, we propose DrBO (DAG recovery via Bayesian Optimization)-a novel DAG learning framework leveraging Bayesian optimization (BO) to find high-scoring DAGs. We show that, by sophisticatedly choosing the promising DAGs to explore, we can find higher-scoring ones much more efficiently. To address the scalability issues of conventional BO in DAG learning, we replace Gaussian Processes commonly employed in BO with dropout neural networks, trained in a continual manner, which allows for (i) flexibly modeling the DAG scores without overfitting, (ii) incorporation of uncertainty into the estimated scores, and (iii) scaling with the number of evaluations. As a result, DrBO is computationally efficient and can find the accurate DAG in fewer trials and less time than existing state-of-the-art methods. This is demonstrated through an extensive set of empirical evaluations on many challenging settings with both synthetic and real data. Our implementation is available at https://github.com/baosws/DrBO.

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