A causal process model for algorithmic recourse introduces post-recourse stability conditions and copula-based methods to infer intervention effects from observational or paired data, with a distribution-free fallback when the model is rejected.
Annals of Mathematics and Artificial Intelligence , volume=
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
citation-role summary
background 2
citation-polarity summary
years
2026 3roles
background 2polarities
background 2representative citing papers
The paper derives sharp partial-identification bounds for the probability of necessity on continuous outcomes and shows how copulas can tighten them.
citing papers explorer
-
Causal Algorithmic Recourse: Foundations and Methods
A causal process model for algorithmic recourse introduces post-recourse stability conditions and copula-based methods to infer intervention effects from observational or paired data, with a distribution-free fallback when the model is rejected.
-
Probabilities of Causation for Continuous Outcomes: Bounds and Identification
The paper derives sharp partial-identification bounds for the probability of necessity on continuous outcomes and shows how copulas can tighten them.
- Trust Me, I'm a Doctor?