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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A unified framework derives non-asymptotic bounds on conditional miscoverage in conformal prediction via pointwise and L_p routes and gives a common view of existing methods.
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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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A Unified Theory of Conditional Coverage in Conformal Prediction with Applications
A unified framework derives non-asymptotic bounds on conditional miscoverage in conformal prediction via pointwise and L_p routes and gives a common view of existing methods.