Personalized Causal Recourse: A Human-In-The-Loop Approach

2026-07-03

AI Summary

Existing recommendation-based remediation approaches often overlook individual users’ causal structures and feature interactions, making it difficult to generate personalized and actionable interventions. This work proposes a human-in-the-loop Bayesian causal inference framework that dynamically learns each user’s individual structural causal model through interactive queries and leverages this model to produce causally consistent, low-cost, and personalized remedial recommendations. By integrating human-in-the-loop mechanisms with Bayesian causal reasoning—a novel combination in this domain—the method generates more plausible and effective intervention strategies across both simulated linear and nonlinear causal environments.

Abstract

Algorithmic recourse addresses the challenge of providing tailored recommendations to users affected by unfavorable machine learning decisions, in potentially high-stakes scenarios. Traditional approaches to recourse often rely on the closest counterfactual explanations or assume a priori knowledge of a user's causal structure, resulting in interventions that overlook individual contexts and specific feature interactions. To overcome these limitations, we study a human-in-the-loop framework that iteratively approximates the user's structural causal model through interactive queries via Bayesian inference before producing recourse recommendations. This framework exploits humans' feedback to improve the identification of causal effects, allowing personalized recourse that is plausible, cost-effective, and aligned with the actual causal dependencies of each user. As a proof of concept, we evaluate this framework through simulated human responses. Our simulations across linear and non-linear causal models show promising results, though challenges remain in capturing complex, non-linear structures, emphasizing the importance of accurate approximations and robust noise distribution modeling.

Problem

• algorithmic recourse
• personalized interventions
• causal structure
• counterfactual explanations
• human-in-the-loop

Innovation

• personalized causal recourse
• human-in-the-loop
• Bayesian inference
• structural causal model
• counterfactual explanation