Contextual Causal Bayesian Optimisation

ICLR 2026 Conference Submission2079 Authors

Keywords: Bayesian Optimization, Causality, Optimal Control

TL;DR: We extend causal Bayesin optimization to contextual scenarios and provide the first upper bound on the regret of causal BO methods that covers both contextual and context free setups.

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.

Primary Area: optimization

Submission Number: 2079