Causal Bayesian Optimization via Exogenous Distribution Learning
CoRR(2024)
摘要
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.
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