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.
What's the harm? sharp bounds on the fraction negatively affected by treatment
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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.