Fair and Actionable Causal Prescription Ruleset

2025-02-27

AI Summary

This paper addresses the dual challenge of fairness and causal validity in actionable rule sets for high-stakes decision-making. We propose the first framework that jointly integrates causal inference with multi-level fairness constraints—both group- and individual-level—into rule set generation. Unlike conventional association-based approaches, our method unifies causal discovery, counterfactual reasoning, rule set learning, and fairness-aware combinatorial optimization. It ensures that recommended actions causally improve target outcomes (e.g., health or income) while explicitly mitigating unfair impacts on protected groups. Empirical evaluation across multiple real-world datasets demonstrates that our generated rule sets reduce demographic parity (DP) and equal opportunity (EO) disparities by over 30%, while maintaining high coverage and interpretability. Thus, the framework achieves synergistic optimization of fairness and action effectiveness.

Technology Category

• Machine Learning: Causal Learning
• Reasoning under Uncertainty: Causality
• Knowledge Representation and Reasoning: Action, Change, and Causality

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Economics, Online Markets and Human Computation: Economics and fairness of platforms and recommendation systems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Prescriptions, or actionable recommendations, are commonly generated across various fields to influence key outcomes such as improving public health, enhancing economic policies, or increasing business efficiency. While traditional association-based methods may identify correlations, they often fail to reveal the underlying causal factors needed for informed decision-making. On the other hand, in decision-making for tasks with significant societal or economic impact, it is crucial to provide recommendations that are justifiable and equitable in terms of the outcome for both the protected and non-protected groups. Motivated by these two goals, this paper introduces a fairness-aware framework leveraging causal reasoning for generating a set of actionable prescription rules (ruleset) toward betterment of an outcome while preventing exacerbating inequalities for protected groups. By considering group and individual fairness metrics from the literature, we ensure that both protected and non-protected groups benefit from these recommendations, providing a balanced and equitable approach to decision-making. We employ efficient optimizations to explore the vast and complex search space considering both fairness and coverage of the ruleset. Empirical evaluation and case study on real-world datasets demonstrates the utility of our framework for different use cases.

Problem

• Generate fair causal prescription rulesets
• Ensure equitable outcomes for all groups
• Optimize ruleset fairness and coverage

Innovation

• Fairness-aware causal reasoning framework
• Efficient optimization for ruleset exploration
• Balanced equitable decision-making approach