Causal Bayesian Optimization: Foundations, Methods, and Applications
Causal Bayesian Optimization (CBO) combines causal inference with Bayesian optimization to enable sample-efficient intervention selection in systems with causal structure. This survey provides a systematic review of CBO through a unified BO-loop perspective, showing how causal assumptions shape intervention search spaces, surrogate models, acquisition functions, and decision policies. We organize existing methods by graph and system-knowledge assumptions, environment, intervention representation, surrogate architecture, and decision rule, and connect CBO to causal bandits, Bayesian experimental design, safe optimization, policy search, and causal abstraction. We also introduce a reproduci...
| Authors | Chenfeng Huang, Thuy T. Le, Zixuan Ma et al. |
|---|---|
| Primary category | stat.ML |
| Published | 21 September 2026 |
| Last updated at source | 21 September 2026 |
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