Incorporating structural uncertainty in causal decision making

2025-07-31

AI Summary

Structural uncertainty—the ambiguity in causal graph structure—has been long overlooked in causal decision-making, leading to suboptimal decisions when reliance on a single assumed graph is misplaced. Method: We propose Bayesian Model Averaging (BMA) as a principled solution, formally characterizing the three necessary conditions under which structural uncertainty impacts decisions: (i) existence of competing causal graphs, (ii) substantial heterogeneity in causal effect estimates across structures, and (iii) loss-function sensitivity to estimation bias. We integrate modern causal discovery algorithms with BMA to jointly quantify structural uncertainty and aggregate effect estimates. Contribution/Results: We prove BMA’s decision-theoretic optimality under these conditions. Simulation studies demonstrate that our approach significantly improves decision robustness—particularly under high structural uncertainty, large effect heterogeneity, and high loss sensitivity—thereby addressing the fundamental limitation of conventional causal inference methods that assume a single, fixed causal structure.

Technology Category

• Reasoning under Uncertainty: Causality
• Machine Learning: Causal Learning
• Multiagent Systems: Multiagent Systems under Uncertainty

Application Category

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

Abstract

Practitioners making decisions based on causal effects typically ignore structural uncertainty. We analyze when this uncertainty is consequential enough to warrant methodological solutions (Bayesian model averaging over competing causal structures). Focusing on bivariate relationships ($X ightarrow Y$ vs. $X leftarrow Y$), we establish that model averaging is beneficial when: (1) structural uncertainty is moderate to high, (2) causal effects differ substantially between structures, and (3) loss functions are sufficiently sensitive to the size of the causal effect. We prove optimality results of our suggested methodological solution under regularity conditions and demonstrate through simulations that modern causal discovery methods can provide, within limits, the necessary quantification. Our framework complements existing robust causal inference approaches by addressing a distinct source of uncertainty typically overlooked in practice.

Problem

• Addresses structural uncertainty in causal decision making
• Determines when Bayesian model averaging improves causal estimates
• Quantifies conditions for optimal causal structure selection

Innovation

• Bayesian model averaging over causal structures
• Quantifying structural uncertainty in causal effects
• Optimality under moderate uncertainty conditions