Multi-Objective Multi-Fidelity Bayesian Optimization with Causal Priors

• URL: http://arxiv.org/abs/2602.00788v1
• Date: Sat, 31 Jan 2026 15:53:22 GMT
• Title: Multi-Objective Multi-Fidelity Bayesian Optimization with Causal Priors
• Authors: Md Abir Hossen, Mohammad Ali Javidian, Vignesh Narayanan, Jason M. O'Kane, Pooyan Jamshidi,
• Abstract summary: We propose RESCUE, a multi-objective MFBO method that incorporates causal calculus to systematically address this challenge.<n>We show that RESCUE improves sample efficiency over state-of-the-art MF optimization methods on synthetic and real-world problems in robotics, machine learning (AutoML), and healthcare.
• Score: 13.649714557575178
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multi-fidelity Bayesian optimization (MFBO) accelerates the search for the global optimum of black-box functions by integrating inexpensive, low-fidelity approximations. The central task of an MFBO policy is to balance the cost-efficiency of low-fidelity proxies against their reduced accuracy to ensure effective progression toward the high-fidelity optimum. Existing MFBO methods primarily capture associational dependencies between inputs, fidelities, and objectives, rather than causal mechanisms, and can perform poorly when lower-fidelity proxies are poorly aligned with the target fidelity. We propose RESCUE (REducing Sampling cost with Causal Understanding and Estimation), a multi-objective MFBO method that incorporates causal calculus to systematically address this challenge. RESCUE learns a structural causal model capturing causal relationships between inputs, fidelities, and objectives, and uses it to construct a probabilistic multi-fidelity (MF) surrogate that encodes intervention effects. Exploiting the causal structure, we introduce a causal hypervolume knowledge-gradient acquisition strategy to select input-fidelity pairs that balance expected multi-objective improvement and cost. We show that RESCUE improves sample efficiency over state-of-the-art MF optimization methods on synthetic and real-world problems in robotics, machine learning (AutoML), and healthcare.

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