Graph Agnostic Causal Bayesian Optimisation
This addresses the challenge of optimizing causal systems with unknown structures for researchers and practitioners in causal inference and optimization, representing a novel approach rather than an incremental improvement.
The paper tackles the problem of globally optimizing a target variable in an unknown causal graph using interventions, formalized as Causal Bayesian Optimization (CBO), and proposes Graph Agnostic Causal Bayesian Optimization (GACBO) to actively discover causal structures while balancing exploration and exploitation. It shows that GACBO outperforms baselines in simulated experiments and real-world applications, though no concrete numbers are provided.
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.