Multi-Objective Causal Bayesian Optimization

2025-02-20

AI Summary

This work addresses high-cost, multi-objective intervention decision-making by efficiently identifying Pareto-optimal intervention variable sets from a known multi-objective causal graph. We propose the first causal Bayesian optimization framework tailored to multi-objective settings: (1) a structural criterion for identifying potentially optimal intervention sets grounded in causal graph topology; (2) relative hypervolume improvement (RHVI) to enable coordinated exploration across objectives; and (3) an intervention-set graph decomposition algorithm to enhance scalability. The method integrates causal modeling, Gaussian process surrogate modeling, and multi-objective optimization theory. Experiments on synthetic and real-world causal graphs demonstrate that our approach converges to superior Pareto fronts with significantly fewer interventions than conventional multi-objective Bayesian optimization, achieving state-of-the-art performance in both efficiency and solution quality.

Technology Category

• Search and Optimization: Combinatorial Optimization
• Reasoning under Uncertainty: Stochastic Optimization
• Constraint Satisfaction and Optimization: Constraint Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

In decision-making problems, the outcome of an intervention often depends on the causal relationships between system components and is highly costly to evaluate. In such settings, causal Bayesian optimization (CBO) can exploit the causal relationships between the system variables and sequentially perform interventions to approach the optimum with minimal data. Extending CBO to the multi-outcome setting, we propose Multi-Objective Causal Bayesian Optimization (MO-CBO), a paradigm for identifying Pareto-optimal interventions within a known multi-target causal graph. We first derive a graphical characterization for potentially optimal sets of variables to intervene upon. Showing that any MO-CBO problem can be decomposed into several traditional multi-objective optimization tasks, we then introduce an algorithm that sequentially balances exploration across these tasks using relative hypervolume improvement. The proposed method will be validated on both synthetic and real-world causal graphs, demonstrating its superiority over traditional (non-causal) multi-objective Bayesian optimization in settings where causal information is available.

Problem

• Optimizes interventions using causal relationships
• Extends CBO to multi-outcome scenarios
• Identifies Pareto-optimal interventions efficiently

Innovation

• Multi-Objective Causal Bayesian Optimization
• Pareto-optimal interventions identification
• Relative hypervolume improvement algorithm