arXiv:2511. 14441v2 Announce Type: replace-cross Abstract: To distinguish Markov equivalent graphs in causal discovery, it is necessary to restrict the structural causal model.
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.
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.
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.
arXiv:2503.17894v4 Announce Type: replace-cross Abstract: We propose a generative learner for estimating conditional average treatment effects and characterizing the full distribution of these effect...
arXiv:2606. 31284v1 Announce Type: new Abstract: Quantile regression aims to estimate the conditional quantiles of a response variable from observed data.
arXiv:2607. 04431v2 Announce Type: replace-cross Abstract: Quantile regression provides a powerful tool for summarizing the conditional distribution of a real-valued random variable (r.
arXiv:2607. 04431v1 Announce Type: cross Abstract: Quantile regression provides a powerful tool for summarizing the conditional distribution of a real valued random variable (r.
arXiv:2606. 25188v1 Announce Type: new Abstract: Efficient uncertainty quantification (UQ) is essential for trustworthy large-scale learning.
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.
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.
arXiv:2503.17894v3 Announce Type: replace-cross Abstract: We propose generative learner for estimating heterogeneous treatment effects and characterizing the full distribution of causal effects. The...