Improved Causal Bayesian Optimization Algorithm with Counter-noise Acquisition Function and Supervised Prior Estimation

Abstract Causality is an important element in decision-making and interventions are required to optimize results of target values. In this paper, based on the model Causal Bayesian Optimization, a counter-noise version acquisition function proposed new prior estimation algorithms including Support Vector Regression, Ridge Regression Random Forest evaluated. This paper provides improved framework facilitate causal inference optimization processes.