Contextual Causal Bayesian Optimisation
This work addresses the challenge of optimizing intervention policies in complex, high-dimensional environments, which is incremental as it unifies and extends existing methods.
The paper tackles the problem of designing intervention policies to maximize a target variable by introducing a unified framework for contextual and causal Bayesian optimization, which leverages observed contexts and causal graphs. The proposed algorithm achieves sublinear regret and reduces sample complexity in high-dimensional settings, as demonstrated by experimental results.
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.