Fair and Actionable Causal Prescription Ruleset

TL;DR

This paper presents a fairness-aware causal framework for generating actionable prescription rules that improve outcomes while ensuring equity across protected and non-protected groups.

Contribution

It introduces a novel causal reasoning approach that incorporates fairness metrics to create balanced and justifiable decision-making rules.

Findings

Effective in improving outcomes with fairness considerations

Balances benefits across protected and non-protected groups

Demonstrated utility on real-world datasets

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.