Transferring Information Across Interventions in Causal Bayesian Optimization

Researchers present graph-coupled causal Bayesian optimization, a method that improves expensive system optimization by sharing information across related interventions through a causal kernel. The approach demonstrates logarithmic information gains and cleanly separates optimization, causal estimation, and intervention selection errors, with strongest performance when direct interventions are unavailable.

This research addresses a fundamental limitation in Bayesian optimization: the inability to leverage shared causal mechanisms across different experiments or interventions. Traditional Bayesian optimization treats control variables as black-box inputs, while existing causal approaches learn intervention effects in isolation despite underlying mechanistic similarities. The proposed graph-coupled method bridges this gap by binding different intervention effects through uncertainty about shared causal parameters, creating a causal kernel that propagates evidence across the intervention space.

The theoretical contributions are substantial. For linear Gaussian causal models, the method achieves a rank-bounded causal kernel determined by the number of shared parameters rather than intervention options, yielding logarithmic information-gain bounds regardless of optimization horizon length. This represents a significant computational efficiency improvement over existing methods. The regret analysis cleanly decomposes error into three interpretable components—optimization error, causal estimation error, and intervention set selection—providing actionable insights for practitioners.

The practical implications extend beyond academic interest. Real-world optimization problems often involve expensive interventions where budget constraints prevent exhaustive exploration. The method's ability to transfer information across related interventions directly addresses this constraint, particularly when direct parent-node interventions are impossible and sparse data must be reused across multiple candidates. This capability resonates with applications in drug discovery, materials science, and industrial process optimization where intervention budgets are severely limited.

Future development hinges on extending these results to nonlinear systems and adaptive intervention selection. The current focus on Gaussian systems, while theoretically clean, limits immediate applicability to complex real-world domains. Success in these extensions could establish this approach as a standard tool for expensive optimization problems where causal structure is known or learnable.

• →Graph-coupled causal Bayesian optimization enables information transfer across related interventions through shared causal parameters
• →Linear Gaussian causal models achieve logarithmic information gains independent of optimization horizon
• →Regret analysis cleanly separates optimization, causal estimation, and intervention selection errors
• →Method shows strongest gains when direct parent interventions are unavailable and data is sparse
• →Theoretical framework supports extensions to nonlinear and adaptive intervention scenarios