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.
Machine learning , volume=
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An augmented kernel ridge regression estimator separates linear and nonlinear components to achieve sharp oracle inequalities and minimax optimal prediction risk under general kernels.
TAP couples a learner-conditioned policy with diffusion inpainting to generate and selectively inject high-utility tabular augmentations, yielding up to 15.6 pp accuracy gains and 32% RMSE reduction on seven datasets under severe scarcity.
Causal stability selection identifies treatment effect modifiers with a non-asymptotic bound on expected false positives by integrating cross-fitted CATE estimation and stability selection.
Proposes a calibration-based estimator for transported average treatment effects that is consistent under correct specification and achieves semiparametric efficiency with large observational data.
citing papers explorer
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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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Adaptive Kernel Ridge Regression with Linear Structure: Sharp Oracle Inequalities and Minimax Optimality
An augmented kernel ridge regression estimator separates linear and nonlinear components to achieve sharp oracle inequalities and minimax optimal prediction risk under general kernels.
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Active Tabular Augmentation via Policy-Guided Diffusion Inpainting
TAP couples a learner-conditioned policy with diffusion inpainting to generate and selectively inject high-utility tabular augmentations, yielding up to 15.6 pp accuracy gains and 32% RMSE reduction on seven datasets under severe scarcity.
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Causal Stability Selection
Causal stability selection identifies treatment effect modifiers with a non-asymptotic bound on expected false positives by integrating cross-fitted CATE estimation and stability selection.
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Transporting treatment effects by calibrating large-scale observational outcomes
Proposes a calibration-based estimator for transported average treatment effects that is consistent under correct specification and achieves semiparametric efficiency with large observational data.