Causal Bayesian Optimization via Exogenous Distribution Learning

Maximizing a target variable as an operational objective in a structured causal model is an important problem. Existing Causal Bayesian Optimization (CBO) methods either rely on hard interventions that alter the causal structure to maximize the reward; or introduce action nodes to endogenous variables so that the data generation mechanisms are adjusted to achieve the objective. In this paper, a novel method is introduced to learn the distribution of exogenous variables, which is typically ignored or marginalized through expectation by existing methods. Exogenous distribution learning improves the approximation accuracy of structured causal models in a surrogate model that is usually trained with limited observational data. Moreover, the learned exogenous distribution extends existing CBO to general causal schemes beyond Additive Noise Models (ANM). The recovery of exogenous variables allows us to use a more flexible prior for noise or unobserved hidden variables. A new CBO method is developed by leveraging the learned exogenous distribution. Experiments on different datasets and applications show the benefits of our proposed method.