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=
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The paper derives sharp partial-identification bounds for the probability of necessity on continuous outcomes and shows how copulas can tighten them.
Derives sharp bounds on the proportion of physicians outperforming trial recommendations using nested randomized and observational data under the assumption that no physician strategy is worse than the trial's inferior arm.
citing papers explorer
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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.
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Trust Me, I'm a Doctor?
Derives sharp bounds on the proportion of physicians outperforming trial recommendations using nested randomized and observational data under the assumption that no physician strategy is worse than the trial's inferior arm.