Causal EpiNets provides a neural method for finite-sample PNS bounds that satisfies structural constraints by construction and achieves nominal coverage via precision-corrected epistemic uncertainty quantification.
arXiv preprint arXiv:2205.10327 , year =
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Develops improved Fréchet-Hoeffding-style bounds and nonparametric estimators for the fraction negatively affected (FNA) by treatment, using Pearson correlation between potential outcomes as a sensitivity parameter.
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Causal EpiNets: Precision-corrected Bounds on Individual Treatment Effects using Epistemic Neural Networks
Causal EpiNets provides a neural method for finite-sample PNS bounds that satisfies structural constraints by construction and achieves nominal coverage via precision-corrected epistemic uncertainty quantification.
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Quantifying Individual Risk for Binary Outcomes
Develops improved Fréchet-Hoeffding-style bounds and nonparametric estimators for the fraction negatively affected (FNA) by treatment, using Pearson correlation between potential outcomes as a sensitivity parameter.