CUBO: Uncertainty-Aware Causal Policy Learning via Bayesian Bootstrap

Classical causal policy learning methods seek treatment rules via empirical welfare maximization. However, this can select a rule with high estimated welfare but substantial downside uncertainty, resulting in poor generalizability of the policy. We propose CUBO, an uncertainty-aware framework for causal policy learning by controlling policy-targeted uncertainty measured by welfare posterior distribution. CUBO combines common Bayesian-bootstrap weights with a nuisance posterior, inducing a joint law of estimated welfare across candidate policies, and the policy is learned by maximizing a reward that combines posterior mean welfare with lower-tail conditional value-at-risk (CVaR). We establish theoretical guarantee on welfare regret bound, and the performance is comprehensively evaluated on IHDP and ACIC datasets. This work provides a novel policy uncertainty quantification from Bayesian perspective, filling the gap of existing methods to learn trustworthy causal policy in real-world applications.