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[2602.14791] Extending Multi-Source Bayesian Optimization With Causality Principles
Summary
This article presents a novel approach to Multi-Source Bayesian Optimization (MSBO) by integrating causality principles, resulting in a new algorithm called Multi-Source Causal Bayesian Optimization (MSCBO).
Why It Matters
The integration of causality into MSBO enhances optimization efficiency and decision-making in complex scenarios, such as clinical trials and policy-making. This advancement could lead to significant improvements in various fields that rely on optimization techniques, making it a relevant contribution to machine learning.
Key Takeaways
- MSCBO combines Multi-Source Bayesian Optimization with Causal principles.
- The new algorithm improves optimization efficiency and reduces computational complexity.
- Causality integration allows for better modeling of variable dependencies.
- MSCBO demonstrates robustness across synthetic and real-world datasets.
- The approach facilitates dimensionality reduction and lowers operational costs.
Computer Science > Machine Learning arXiv:2602.14791 (cs) [Submitted on 16 Feb 2026] Title:Extending Multi-Source Bayesian Optimization With Causality Principles Authors:Luuk Jacobs, Mohammad Ali Javidian View a PDF of the paper titled Extending Multi-Source Bayesian Optimization With Causality Principles, by Luuk Jacobs and Mohammad Ali Javidian View PDF HTML (experimental) Abstract:Multi-Source Bayesian Optimization (MSBO) serves as a variant of the traditional Bayesian Optimization (BO) framework applicable to situations involving optimization of an objective black-box function over multiple information sources such as simulations, surrogate models, or real-world experiments. However, traditional MSBO assumes the input variables of the objective function to be independent and identically distributed, limiting its effectiveness in scenarios where causal information is available and interventions can be performed, such as clinical trials or policy-making. In the single-source domain, Causal Bayesian Optimization (CBO) extends standard BO with the principles of causality, enabling better modeling of variable dependencies. This leads to more accurate optimization, improved decision-making, and more efficient use of low-cost information sources. In this article, we propose a principled integration of the MSBO and CBO methodologies in the multi-source domain, leveraging the strengths of both to enhance optimization efficiency and reduce computational complexity in higher-dime...