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arXiv Machine Learning By Xin Yu, Shuwei Huang, Jicheng Liu, Jielin Tang, Bolin Wang, Yunxiao Zhang, Tian Zhao

A Location-Invariant Estimator of Extremal Quantile Treatment Effects for Heavy-Tailed Distributions

Read the original on arXiv Machine Learning →

The paper introduces a new estimator for extremal quantile treatment effects (QTEs) that remains invariant under location shifts of heavy‑tailed outcome distributions. It adapts the Fraga estimator of the extreme value index to a causal framework via inverse propensity score weighting and replaces the traditional extrapolation with a difference‑based scheme that cancels the location parameter. The authors prove consistency, asymptotic normality, and provide a variance estimator, with simulations demonstrating location invariance, threshold stability, and correct coverage.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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arXiv Machine Learning
Sep 4

A location-invariant estimator of extremal quantile treatment effects for heavy-tailed distributions

The paper introduces a new estimator for extremal quantile treatment effects (QTEs) in heavy-tailed distributions that remains invariant under common location shifts of the potential outcome distributions. It adapts the Fraga estimator of the extreme value index (EVI) to a causal framework using inverse propensity score weighting and replaces the traditional extrapolation formula with a difference-based scheme that cancels the location parameter. The authors prove consistency, asymptotic normality, and provide a variance estimator, and demonstrate through simulations that the method is location invariant, stable across thresholds, and achieves correct coverage.

By Xin Yu, Shuwei Huang, Jicheng Liu, Jielin Tang, Bolin Wang, Yunxiao Zhang, Tian Zhao
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