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:2111.10106 , year=
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PROXIMA scores proxy reliability via a composite of effect correlation, directional accuracy, and segment fragility, achieving 98.4% decision agreement with an oracle on two public datasets.
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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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PROXIMA: A Reliability Scoring Framework for Proxy Metrics in Online Controlled Experiments
PROXIMA scores proxy reliability via a composite of effect correlation, directional accuracy, and segment fragility, achieving 98.4% decision agreement with an oracle on two public datasets.
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