Causal Bayesian Optimization through Exogenous Distribution Learning
The core message of this paper is to propose a novel Causal Bayesian Optimization method, EXCBO, that approximately recovers the endogenous variables in a structured causal model. With the recovered exogenous distribution, the method improves the surrogate model's accuracy in the approximation of structural causal models, leading to enhanced sample efficiency of causal Bayesian optimization.