惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

阮一峰的网络日志
阮一峰的网络日志
Apple Machine Learning Research
Apple Machine Learning Research
量子位
D
DataBreaches.Net
云风的 BLOG
云风的 BLOG
博客园 - 聂微东
博客园_首页
D
Docker
博客园 - 叶小钗
S
SegmentFault 最新的问题
大猫的无限游戏
大猫的无限游戏
钛媒体:引领未来商业与生活新知
钛媒体:引领未来商业与生活新知
J
Java Code Geeks
H
Hackread – Cybersecurity News, Data Breaches, AI and More
A
About on SuperTechFans
博客园 - 三生石上(FineUI控件)
F
Fortinet All Blogs
小众软件
小众软件
aimingoo的专栏
aimingoo的专栏
爱范儿
爱范儿
腾讯CDC
罗磊的独立博客
雷峰网
雷峰网
博客园 - Franky

stat updates on arXiv.org

A Refined Generalization Analysis for Extreme Multi-class Supervised Contrastive Representation Learning Ensemble Distributionally Robust Bayesian Optimisation The Proxy Presumption: From Semantic Embeddings to Valid Social Measures Modulated learning for private and distributed regression with just a single sample per client device Query-efficient model evaluation using cached responses Functional-prior-based approaches to Bayesian PDE-constrained inversion using physics-informed neural networks Optimal Experiments for Partial Causal Effect Identification Order-Agnostic Autoregressive Modelling with Missing Data Grokking or Glitching? How Low-Precision Drives Slingshot Loss Spikes Tuning Derivatives for Causal Fairness in Machine Learning Spherical Flows for Sampling Categorical Data Bayesian Rain Field Reconstruction using Commercial Microwave Links and Diffusion Model Priors GRALIS: A Unified Canonical Framework for Linear Attribution Methods via Riesz Representation Sharp Capacity Thresholds in Linear Associative Memory: From Winner-Take-All to Listwise Retrieval Unified Framework of Distributional Regret in Multi-Armed Bandits and Reinforcement Learning Jacobian-Velocity Bounds for Deployment Risk Under Covariate Drift Self-Attention as Transport: Limits of Symmetric Spectral Diagnostics Perturbation is All You Need for Extrapolating Language Models Adapt or Forget: Provable Tradeoffs Between Adam and SGD in Nonstationary Optimization Realizable Bayes-Consistency for General Metric Losses Graph Convolutional Support Vector Regression for Robust Spatiotemporal Forecasting of Urban Air Pollution Segmenting Human-LLM Co-authored Text via Change Point Detection Stochastic Schrödinger Diffusion Models for Pure-State Ensemble Generation Understanding Self-Supervised Learning via Latent Distribution Matching The Geometric Mechanics of Contrastive Representation Learning: Alignment Potentials, Entropic Dispersion, and Cross-modal Divergence Imbalanced Classification under Capacity Constraints On the Spectral Structure and Objective Equivalence of Orthogonal Multilabel Fisher Discriminants Partially Observed Structural Causal Models First-Order Efficiency for Probabilistic Value Estimation via A Statistical Viewpoint Robust and Fast Training via Per-Sample Clipping
Semiparametric Estimation of Delayed-Outcome Treatment Ef...
[Submitted on 11 Mar 2026 (v1), last revised 3 Sep 2026 (this ve · 2026-03-11 · via stat updates on arXiv.org

View PDF HTML (experimental)

Abstract:The multi-site registry studies, such as Stepped-wedge cluster-randomized trials (SW-CRT), staggered-enrollment RCTs, etc., share a structural feature: the primary long-term outcome is administratively censored for a non-negligible fraction of units, with censoring driven by calendar design rather than by the outcome itself. Standard inverse-probability-of-censoring weighting becomes unstable when observation probabilities $g_{\Delta}$ concentrate near zero for late-crossing units, while parametric mixed-model analyses discard the information in any short-term intermediate measurement and rely on correct specification of the secular time trend. We study semiparametric estimation of the average treatment effect when a short-term surrogate, which is observed for all units and conditionally independent of the censoring mechanism given baseline covariates, is available. Identification takes a nested-integral form in which the outcome regression is marginalized over the conditional surrogate distribution, so the observation mechanism does not enter the target functional as an inverse weight. We show that a density-plug-in one-step debiased machine-learning construction for this functional leaves a second-order cross-product remainder $R_{SY}$ that has no doubly-robust complement in the efficient influence function and is not eliminated by cross-fitting . We propose a surrogate-assisted AIPW estimator (SA-AIPW) that integrates over the empirical surrogate distribution through treatment weighting rather than estimating the conditional surrogate density, and so structurally avoids $R_{SY}$. For clustered data, the estimator is shown to be $\sqrt{J}$-consistent and asymptotically linear under a product-rate double-robustness condition.

Submission history

From: Lin Li [view email]
[v1] Wed, 11 Mar 2026 04:38:35 UTC (48 KB)
[v2] Sun, 15 Mar 2026 19:22:01 UTC (47 KB)
[v3] Tue, 24 Mar 2026 03:25:36 UTC (51 KB)
[v4] Tue, 31 Mar 2026 23:39:57 UTC (51 KB)
[v5] Thu, 3 Sep 2026 17:58:10 UTC (53 KB)