Causal Algorithmic Recourse: Foundations and Methods

TL;DR

This paper introduces a causal framework for algorithmic recourse, modeling how individuals can reverse negative AI decisions through repeated interventions, and develops methods to infer recourse effects from observational and recourse data.

Contribution

It proposes a novel causal approach to model and analyze algorithmic recourse, including stability conditions and copula-based algorithms for effect inference.

Findings

The framework enables reasoning about recourse stability from observational data.

Copula-based algorithms effectively infer recourse effects under stability conditions.

Distribution-free methods learn recourse effects when copula models are rejected.

Abstract

The trustworthiness of AI decision-making systems is increasingly important. A key feature of such systems is the ability to provide recommendations for how an individual may reverse a negative decision, a problem known as algorithmic recourse. Existing approaches treat recourse outcomes as counterfactuals of a fixed unit, ignoring that real-world recourse involves repeated decisions on the same individual under possibly different latent conditions. We develop a causal framework that models recourse as a process over pre- and post-intervention outcomes, allowing for partial stability and resampling of latent variables. We introduce post-recourse stability conditions that enable reasoning about recourse from observational data alone, and develop a copula-based algorithm for inferring the effects of recourse under these conditions. For settings where paired observations of the same individual before and after intervention are available (called recourse data), we develop methods for inferring copula parameters and performing goodness-of-fit testing. When the copula model is rejected, we provide a distribution-free algorithm for learning recourse effects directly from recourse data. We demonstrate the value of the proposed methods on real and semi-synthetic datasets.