Personalized Causal Recourse: A Human-In-The-Loop Approach
2026-07-03 • Artificial Intelligence
Artificial IntelligenceHuman-Computer Interaction
AI summaryⓘ
The authors study how to give personalized advice to people who get unfavorable decisions from machine learning systems. Instead of assuming what causes their situation, they ask users questions to gradually learn the real causes behind their issues. This interactive approach helps make recommendations that better fit each person’s unique context and are more realistic and affordable. They tested their method using simulations and found it works well for simple and some complex cases, though harder scenarios remain challenging.
algorithmic recoursecounterfactual explanationsstructural causal modelBayesian inferencehuman-in-the-loopcausal effectspersonalized recommendationsmachine learning decisionsnon-linear models
Authors
Denise Tampieri, Giovanni De Toni, Paolo Giudici
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.