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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Sharp bounds are derived on the proportion of physicians whose personal strategies perform at least as well as the trial's better average treatment, using nested randomized and observational data from the same population.
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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.
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Trust Me, I'm a Doctor?
Sharp bounds are derived on the proportion of physicians whose personal strategies perform at least as well as the trial's better average treatment, using nested randomized and observational data from the same population.