Formulates privacy-constrained advertising measurement as a robust causal decision problem under signal loss and derives a sharp decision frontier separating certifiable from unresolved incrementality claims.
arXiv preprint arXiv:2111.10106 , year=
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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.
Large-scale study finds that counterfactual metrics on semi-simulated data do not select the same estimators as observable metrics on real data, and benchmark rankings fail to transfer.
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.
Proposes CHAUN with shared embeddings and cross-head attention plus RA-IPS for robust ITE estimation under unobserved confounding, reporting up to 25.6% QINI gains on public and production datasets.
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Privacy-Robust Incrementality Measurement for Advertising Systems under Signal Loss
Formulates privacy-constrained advertising measurement as a robust causal decision problem under signal loss and derives a sharp decision frontier separating certifiable from unresolved incrementality claims.
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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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Real vs. Semi-Simulated: Rethinking Evaluation for Treatment Effect Estimation
Large-scale study finds that counterfactual metrics on semi-simulated data do not select the same estimators as observable metrics on real data, and benchmark rankings fail to transfer.
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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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Cross-Head Attention Uplift Network with Inverse Propensity Score under Unobserved Confounding
Proposes CHAUN with shared embeddings and cross-head attention plus RA-IPS for robust ITE estimation under unobserved confounding, reporting up to 25.6% QINI gains on public and production datasets.