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 of Causal Bayesian Optimization, a counter-noise version of acquisition function is proposed and new prior estimation algorithms including Support Vector Regression, Ridge Regression and Random Forest are evaluated. This paper provides an improved framework to facilitate causal inference and optimization processes.
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