Causal Preference Elicitation

2026-02-01

AI Summary

This work proposes a Bayesian active learning framework for causal discovery under limited expert query budgets. The approach iteratively queries the existence and direction of local edges via triplet relationships to efficiently optimize the posterior over directed acyclic graphs (DAGs). It introduces a novel triplet likelihood model to account for noise in expert judgments and selects the most informative queries based on expected information gain. By integrating particle-based approximation with Bayesian inference, the method significantly improves DAG structure recovery accuracy on synthetic graphs, protein signaling networks, and human gene perturbation data. Moreover, it accelerates posterior convergence and enhances causal effect identification under stringent query constraints.

Technology Category

• Machine Learning: Causal Learning
• Reasoning under Uncertainty: Causality
• Constraint Satisfaction and Optimization: Constraint Learning and Acquisition

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

We propose causal preference elicitation, a Bayesian framework for expert-in-the-loop causal discovery that actively queries local edge relations to concentrate a posterior over directed acyclic graphs (DAGs). From any black-box observational posterior, we model noisy expert judgments with a three-way likelihood over edge existence and direction. Posterior inference uses a flexible particle approximation, and queries are selected by an efficient expected information gain criterion on the expert's categorical response. Experiments on synthetic graphs, protein signaling data, and a human gene perturbation benchmark show faster posterior concentration and improved recovery of directed effects under tight query budgets.

Problem

• causal discovery
• preference elicitation
• expert-in-the-loop
• directed acyclic graphs
• active querying

Innovation

• causal discovery
• Bayesian active learning
• expert-in-the-loop
• directed acyclic graphs
• preference elicitation