Graph Agnostic Causal Bayesian Optimisation
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Abstract (arXiv)
We study the problem of globally optimising a target variable of an unknown causal graph on which a sequence of soft or hard interventions can be performed. The problem of optimising the target variable associated with a causal graph is formalised as Causal Bayesian Optimisation (CBO). We study the CBO problem under the cumulative regret objective with unknown causal graphs for two settings, namely structural causal models with hard interventions and function networks with soft interventions. We propose Graph Agnostic Causal Bayesian Optimisation (GACBO), an algorithm that actively discovers the causal structure that contributes to achieving optimal rewards. GACBO seeks to balance exploiting the actions that give the best rewards against exploring the causal structures and functions. To the best of our knowledge, our work is the first to study causal Bayesian optimization with cumulative regret objectives in scenarios where the graph is unknown or partially known. We show our proposed algorithm outperforms baselines in simulated experiments and real-world applications.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 24 of its 50 references that are in the map. First posted on arXiv 5 Nov 2024.
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24 of 50 references.
References · 24
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- 2022Active Bayesian Causal Inferencecited by 34
12 more
- 2021Bayesian Optimization of Function Networkscited by 33
- 2022Model-based Causal Bayesian Optimizationcited by 26
- 2022Differentiable Causal Discovery Under Latent Interventionscited by 21
- 2019Optimal experimental design via Bayesian optimization: active causal structure learning for Gaussian process networkscited by 18
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- 2023Causal Bandits without Graph Learningcited by 10
- 2023Additive Causal Bandits with Unknown Graphcited by 10
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