Extreme Value Theory

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A location-invariant estimator of extremal quantile treatment effects for heavy-tailed distributions
Sep 3, 2026Xin Yu, Shuwei Huang, Jicheng Liu +4Causal Effect Estimation Heavy-Tailed Distributions
Huazhong University of Science and Technology · Tencent
Tail-Shape Estimation in LLM Evaluation Is Fragile: A Protocol for Diagnosing False Positives
Jun 15, 2026Luca ZhouLLM Evaluation Language Model Safety Evaluation
Sapienza University of Rome
Gradient boosting for extremes: sampling theory and application to insurance
Jun 12, 2026Stéphane Lhaut, Olivier LopezGradient-Boosted Decision Trees Heavy-Tailed Distributions
1CREST, CNRS, Ecole polytechnique, Groupe ENSAE-ENSAI, ENSAE Paris, Institut Polytechnique de Paris, Palaiseau, France
Out-of-Distribution generalization of quantile regression with heavy tailed inputs: an SVM approach
May 29, 2026Baptiste Leroux, Clément Dombry, Anne SabourinQuantile Regression OOD Generalization
Université Paris Cité, CNRS, MAP5, F-75006 Paris, France · Université Marie et Louis Pasteur, CNRS, LmB (UMR 6623), F-25000 Besançon, France · Université Paris Cité, Université Paris Saclay, ENS Paris Saclay, CNRS, SSA, INSERM, Centre Borelli, F-75006, Paris, France
When Softmax Fails at the Top: Extreme Value Corrections for InfoNCE
May 29, 2026Melihcan Erol, Suat Evren, Oktay Ozel +3Contrastive Learning Representation Learning
Massachusetts Insititute of Technology, Cambridge, Massachusetts · Boston University, Boston, Massachusetts.
Stop Suppressing the Tail: Causal Inference for Extreme Events
May 26, 2026Eichi UeharaCausal Effect Estimation Causal Inference
Extrapolation in Statistical Learning with Extreme Value Theory
May 3, 2026Sebastian Engelke, Nicola Gnecco, Anne SabourinOOD Generalization Statistical Learning Theory
Research Institute for Statistics and Information Science, University of Geneva, Switzerland · Department of Mathematics, Imperial College London, United Kingdom · Université Paris Cité, Université Paris-Saclay, ENS Paris-Saclay, CNRS, SSA, INSERM, Centre Borelli, France
SHIFT: Robust Double Machine Learning for Average Dose-Response Functions under Heavy-Tailed Contamination
Apr 30, 2026Eichi UeharaDouble ML Robust Regression
Independent researcher.
EVT-Based Generative AI for Tail-Aware Channel Estimation
Apr 27, 2026Parmida Valiahdi, Niloofar Mehrnia, Walid Saad +1Synthetic Data Augmentation Wireless Communications
∗Department of Electrical and Electronics Engineering, Koc University, Istanbul, Turkey · †Department of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, Stockholm, Sweden · ‡Department of Electrical and Computer Engineering, Virginia Tech, Virginia, United States
Multi-output Extreme Spatial Model for Complex Aircraft Production Systems
Apr 24, 2026Cheolhei Lee, Xing Wang, Xiaowei Yue +1Extreme Value Theory
Grado Department of Industrial and Systems Engineering, Virginia Polytechnic Institute and State University, Blacksburg, Virginia 24061 · Department of Mathematics, Illinois State University, Normal, Illinois 61790 · Department of Industrial Engineering, Tsinghua University, Beijing, 100084 China +1

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

Xin Yu, Shuwei Huang, Jicheng Liu, Jielin Tang+3

Huazhong University of Science and Technology · Tencent

Causal Effect Estimation Heavy-Tailed Distributions Extreme Value Theory

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Quantile treatment effects (QTEs) measure the effect of a treatment on the distribution of an outcome, and their estimation at extreme quantile levels is of central interest in applications where the target quantiles lie far beyond the range of the data. For heavy-tailed potential outcomes, existing extremal QTE estimators rely on extrapolation combined with a causal extreme value index (EVI) estimator, but the resulting estimator is not invariant under a common location shift of the potential outcome distributions, even though the population QTE is. We address this issue in two steps. First, we adapt the location-invariant Fraga estimator of the EVI to the causal setting using inverse propensity score weighting. Second, we replace the original extrapolation formula with a difference-based scheme, under which the location parameter cancels when quantile differences are taken. The resulting QTE estimator is therefore location invariant. We establish the consistency and asymptotic normality of the proposed extremal QTE estimators, and provide a consistent variance estimator, leading to asymptotically valid inference. A simulation study confirms the location invariance, the stability with respect to the threshold, and the coverage of the proposed methods.

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