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SHIFT estimator improves robust double machine learning for heavy-tailed data

By PulseAugur Editorial · Summary by gemini-2.5-flash-lite from 2 sources

Researchers have developed SHIFT, a new robust estimator for Double Machine Learning (DML) pipelines designed to handle heavy-tailed data contamination. SHIFT combines cross-fit nuisance orthogonalization with a kernel-local Welsch-loss second stage and a defensive Ordinary Least Squares refit. This approach significantly improves accuracy in the presence of outliers, reducing Root Mean Squared Error (RMSE) from 1.03 to 0.33 in stress tests and achieving a high F1 score for outlier mask recovery. AI

Summary written by gemini-2.5-flash-lite from 2 sources. How we write summaries →

IMPACT Introduces a robust statistical method for handling contaminated data in machine learning pipelines, potentially improving reliability in real-world applications.

RANK_REASON This is a research paper detailing a new statistical estimation method.

Read on arXiv stat.ML →

COVERAGE [2]

  1. arXiv cs.LG TIER_1 · Eichi Uehara ·

    SHIFT: Robust Double Machine Learning for Average Dose-Response Functions under Heavy-Tailed Contamination

    arXiv:2605.00176v1 Announce Type: cross Abstract: Double-machine-learning pipelines for the Average Dose-Response Function rely on kernel-weighted local-linear smoothers, which inherit unbounded functional influence: a single outlier within a kernel window biases the curve across…

  2. arXiv stat.ML TIER_1 · Eichi Uehara ·

    SHIFT: Robust Double Machine Learning for Average Dose-Response Functions under Heavy-Tailed Contamination

    Double-machine-learning pipelines for the Average Dose-Response Function rely on kernel-weighted local-linear smoothers, which inherit unbounded functional influence: a single outlier within a kernel window biases the curve across the entire window. We introduce SHIFT (Self-calib…

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