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
arXiv:2609.16931v1 Announce Type: cross
Abstract: We propose Low-Rank Quantile Surfaces (LRQS), a bivariate causal model in which, in the causal direction, an unknown monotone transformation of the c...
By Ryo Kamimura, Thong Pham
arXiv:2608. 08204v1 Announce Type: cross Abstract: This work proposes deep nonparametric Instrumental variable quantile regression (IVQR), a two-stage estimator that combines conditional diffusion modeling with a kernel-smoothed conditional moment formulation.
By Xingdong Feng, Xinhong Jiang, Yuling Jiao, Lican Kang, Junwei Liu
arXiv:2608. 16864v1 Announce Type: cross Abstract: In survival analysis the way covariates act on the risk of an event often differs between early and late failure times, yet hazard- and mean-based summaries collapse this variation into a single number.
By Shuai Huang, Zhe Qu, Zhaowei Hua, Guohao Shen, Rui Tang, Hongtu Zhu
arXiv:2609.22383v1 Announce Type: cross
Abstract: Instrumental variable (IV) methods address treatment endogeneity, but with non-compliance and heterogeneous treatment effects a binary instrument gen...
By Zixuan Yao, Guosheng Yin
arXiv:2608. 19383v1 Announce Type: cross Abstract: Average dose-response functions are widely used to summarize causal effects of continuous treatments, but most existing methods assume that the observed sample represents the target population.
By Jay Jojo Cheng, Guanhua Chen
arXiv:2606. 21185v2 Announce Type: replace-cross Abstract: There is a precise sense in which drawing causal inferences from observational data is hard, even when identifiability is assumed.
By Alexis Bellot
The paper introduces a new estimand for conditional distributional treatment effects that captures how treatments influence the entire outcome distribution, including variance and tail risks, in a covariate-dependent manner. It presents a doubly robust estimator that is minimax optimal locally and uses it to construct a test for global homogeneity of conditional potential outcome distributions. The test accommodates discrepancies beyond the maximum mean discrepancy, guarantees valid type‑1 error, is consistent against fixed alternatives, and includes a computationally efficient, permutation‑free algorithm with exact closed‑form expressions for two natural discrepancies.
By Saksham Jain, Alex Luedtke
arXiv:2606. 00265v1 Announce Type: cross Abstract: We study quantile regression in an extrapolation regime where the covariate takes unusually large values.
By Baptiste Leroux, Cl\'ement Dombry, Anne Sabourin
arXiv:2609. 20749v1 Announce Type: cross Abstract: Location estimation exhibits markedly different finite-sample behavior across noise distributions: regular families typically yield root-\(n\) rates, whereas compactly supported laws may admit faster, boundary-driven rates.
By Qiaosen Wang, Chao Gao
arXiv:2608. 15290v1 Announce Type: cross Abstract: The increasing availability of large and complex datasets across many scientific disciplines has led to widespread adoption of machine learning (ML) for prediction.
By Mandy Yao (University of Toronto), Meredith Franklin (University of Toronto)
arXiv:2607. 08444v1 Announce Type: cross Abstract: In this paper, we study quantile-based distributional reinforcement learning from the perspective of statistical efficiency.
By Zijie Cheng, Yang Peng, Zhihua Zhang