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arXiv · 2301.12412

Contextual Causal Bayesian Optimisation

Vahan Arsenyan·Antoine Grosnit·Haitham Bou-Ammar·Arnak Dalalyan
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Abstract

We introduce a unified framework for contextual and causal Bayesian optimisation, which aims to design intervention policies maximising the expectation of a target variable. Our approach leverages both observed contextual information and known causal graph structures to guide the search. Within this framework, we propose a novel algorithm that jointly optimises over policies and the sets of variables on which these policies are defined. This thereby extends and unifies two previously distinct approaches: Causal Bayesian Optimisation and Contextual Bayesian Optimisation, while also addressing their limitations in scenarios that yield suboptimal results. We derive worst-case and instance-dependent high-probability regret bounds for our algorithm. We report experimental results across diverse environments, corroborating that our approach achieves sublinear regret and reduces sample complexity in high-dimensional settings.

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Vahan Arsenyan, Antoine Grosnit, Haitham Bou-Ammar, Arnak Dalalyan. 2023-01-29. Contextual Causal Bayesian Optimisation. https://arxiv.org/abs/2301.12412

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