The authors develop a causal framework for algorithmic recourse — recommendations for how an individual can reverse a negative AI decision (e.g., denial of a loan). Traditional approaches treat recourse as a single counterfactual intervention on a fixed unit, but real-world recourse involves repeated decisions under possibly different latent conditions. They model recourse as a process with pre- and post-intervention outcomes, allowing latent variables to partially resample. Under "post-recourse stability" conditions, they show you can infer recourse effects from observational data alone using a copula-based algorithm. When paired observations (before/after intervention on the same person) are available, they provide methods for copula parameter inference and goodness-of-fit testing, plus a distribution-free algorithm when the copula model is rejected.
Main takeaways:
- Algorithmic recourse (how to reverse a negative decision) is typically modeled as a one-time counterfactual, but real recourse involves repeated decisions with potentially resampled latent conditions.
- The paper models recourse as a process over pre- and post-intervention outcomes, with partial stability and latent resampling.
- Under "post-recourse stability" conditions, recourse effects can be inferred from observational data alone via a copula-based method.
- When "recourse data" (paired before/after observations) are available, the authors provide copula parameter inference, goodness-of-fit testing, and a distribution-free fallback.
- Demonstrations on real and semi-synthetic datasets show the methods' practical value.