The Role of Causality in Algorithmic Recourse

2026-07-30

AI Summary

This work addresses a critical limitation in existing algorithmic attribution methods, which focus solely on flipping model predictions while neglecting how recommendations can genuinely enhance individuals’ underlying qualifications. Such oversight often triggers strategic gaming and necessitates frequent model retraining. To overcome this, the study introduces structural causal models into algorithmic attribution for the first time, employing a causal intervention framework to characterize feature interactions and their effects on true outcomes. By integrating iterative dynamics to solve the resulting non-convex optimization problem, the authors propose a causal attribution strategy that achieves stable equilibrium. Experiments on both semi-synthetic and real-world credit datasets demonstrate that the method substantially outperforms empirical risk minimization, effectively mitigating distributional shifts caused by strategic behavior and significantly reducing the need for model retraining.

Abstract

Algorithmic recourse aims to provide individuals with actionable changes to improve their predicted outcomes in high-stakes classification settings, such as loan and mortgage applications. However, most existing approaches focus only on flipping a model's prediction, without accounting for whether the recommended changes lead to genuine improvement in an individual's true qualifications or merely enable strategic gaming of the classifier. Consequently, deployed recourse policies can induce behavioral responses that degrade predictive accuracy and become ineffective after model retraining. In this work, we formalize this failure mode through a causal performative framework for recourse. We model how recourse actions propagate through a structural causal model, capturing interactions among features as well as their effect on the true label. These causal responses induce a non-convex optimization problem, even under standard convex losses. We characterize conditions under which performatively stable solutions exist and can be efficiently computed via simple iterative dynamics. Our analysis reveals that recourse policies that ignore causal structure can induce large, misaligned behavioral responses, whereas causal recourse leads to stable equilibria that reduce incentives for gaming. Experiments on both semi-synthetic and real credit datasets demonstrate that our approach consistently outperforms standard empirical risk minimization while reducing the need for repeated model retraining to accommodate distribution shifts caused by strategic agent behavior.

Problem

• algorithmic recourse
• causality
• strategic gaming
• performative prediction
• distribution shift

Innovation

• causal recourse
• performative stability
• structural causal model
• algorithmic recourse
• strategic behavior