Covariate Balancing

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3 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

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Proximal Balancing for Causal Effect Estimation under Unmeasured Confounding
Sep 30, 2026Yonghan JungCausal Inferences Latent Confounders
School of Information Sciences University of Illinois Urbana-Champaign
JudgeCast: Time Series Forecasting with Experience-Informed Covariate Judgements
Sep 29, 2026Donguk Kwon, Wooseok Jeong, Dongha LeeData-Driven Forecasting Time Series Forecasting
Yonsei University · Konkuk University
Weighted Spline-Expanded Networks with Distributional Balancing for Continuous Treatment Effects
Sep 27, 2026Shucheng Liu, Chan Park, Guanhua ChenHeterogeneous Treatment Effects Covariate Balancing
University of North Carolina at Chapel Hill · University of Illinois Urbana-Champaign · University of Wisconsin–Madison
$t_0$ : A Time-Series Foundation Model for Forecasting with Context
Sep 21, 2026Lucas Meyer, Claudio Sole, Huikan Xiang +6Time Series Foundation Models Probabilistic Forecasting
The Forecasting Company · Macrocosm
Cross-Block Conditioning in Deep Boltzmann Machines for Statistical Data Fusion
Sep 14, 2026Junichiro NiimiCovariate Balancing Discriminative
Meijo University
Neural ODE enhanced linear mixed effect models for estimating complex association patterns of time-varying covariates with the marker trajectory
Aug 30, 2026Zhe Aurore Li, Quentin Clairon, Cécilia Samieri +3Covariates Generalized Linear Model
Univ. Bordeaux, Inserm, BPH, UMR 1219, 33000 Bordeaux, France · INRIA SISTM, 33000 Bordeaux, France · Vaccine Research Institute, 94010 Creteil, France
Evaluating covariate balance for long time horizon Markov decision processes
Jul 16, 2026Joshua Spear, Rebecca Pope, Neil J SebireCovariate Balancing Model-Based Reinforcement Learning
UCL GOS Institute of Child Health · NIHR GOSH UCL BRC
Optimal Mixture-of-Experts Model Averaging for Conditional Generative Models
Jul 5, 2026Shijin Gong, Baihua He, Xinyu ZhangGenerative Models Mixture-Of-Experts
School of Management, University of Science and Technology of China · Academy of Mathematics and Systems Science, Chinese Academy of Sciences
Evaluating Time Series Foundation Models for Electricity Price Forecasting: Contamination Risk, Distributional Shifts, and Covariate Dependence
Jul 2, 2026Zhenghua Pan, Ahmed Aziz EzzatElectricity Price Forecasting Time Series Foundation Models
Department of Industrial & Systems Engineering, Rutgers, The State University of New Jersey, USA.
Gradient boosting for extremes: sampling theory and application to insurance
Jun 12, 2026Stéphane Lhaut, Olivier LopezGradient Boosted Decision Tree Empirical Risk Minimization
1CREST, CNRS, Ecole polytechnique, Groupe ENSAE-ENSAI, ENSAE Paris, Institut Polytechnique de Paris, Palaiseau, France
Generative Frontier Planning for Adaptive Peer-Referral Recruitment under Covariate-Dependent Arrivals
Jun 6, 2026Lingkai Kong, Hezi Jiang, Andrew Ma +3Covariate Balancing Queue Control
Harvard University
Modeling Covariate Transition for Efficient Estimation of Longitudinal Treatment Effects in Randomized Experiments
May 29, 2026Naoki Chihara, Tatsushi Oka, Yasuko Matsubara +2Heterogeneous Treatment Effects Covariate Balancing
SANKEN, The University of Osaka, Osaka, Japan · CyberAgent AI Lab · Keio University, Tokyo, Japan +1
Local Covariate Selection for Average Causal Effect Estimation without Pretreatment and Causal Sufficiency Assumptions
May 20, 2026Zeyu Liu, Zheng Li, Feng Xie +3Covariate Balancing Causal Inferences
Department of Applied Statistics, Beijing Technology and Business University, Beijing, China · Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China · College of Computer Science and Artificial Intelligence, Fudan University, Shanghai, China +2
Investigating simple target-covariate relationships for Chronos-2 and TabPFN-TS
May 12, 2026Gaspard Berthelier, Mariia Baranova, Andrei-Tiberiu Pantea +4Time Series Foundation Models Covariate Balancing
EDF R&D, Palaiseau · Inria Sophia-Antipolis, Université Côte d’Azur · Université Paris Cit'e
DARTS: Targeting Prognostic Covariates in Budget-Constrained Sequential Experiments
May 7, 2026Kateryna Husar, Alexander VolfovskyCovariate Balancing Bayesian Experimental Design
Department of Statistical Science Duke University Durham, NC 27705
Covariate Balancing and Riesz Regression Should Be Guided by the Neyman Orthogonal Score in Debiased Machine Learning
May 7, 2026Masahiro KatoCovariate Balancing Bias-Variance Decomposition
Data Analytics Department, Mizuho-DL Financial Technology, Co., Ltd.
Data-Driven Covariate Selection for Nonparametric and Cycle-Agnostic Causal Effect Estimation
May 7, 2026Ana Leticia Garcez Vicente, Gijs van Seeventer, Saber SalehkaleybarCovariate Balancing Causal Inferences
Leiden University Netherlands
Online Localized Conformal Prediction
May 6, 2026Yuheng Lai, Garvesh RaskuttiOnline Conformal Prediction Conformal Inference
University of Wisconsin - Madison
A Semi-Supervised Kernel Two-Sample Test
May 3, 2026Gyumin Lee, Shubhanshu Shekhar, Ilmun KimTwo-Sample Testing Covariate Balancing
Dept. of Statistics Pennsylvania State University · Dept. of EECS University of Michigan · Dept. of Mathematical Sciences KAIST
Differential Subgroup Discovery: Characterizing Where Two Populations Differ, and Why
Apr 30, 2026Sascha Xu, Jilles VreekenSubgroups Covariate Balancing
CISPA Helmholtz Center for Information Security Saarbrücken, Germany
Improving RCT-Based Treatment Effect Estimation Under Covariate Mismatch via Calibrated Alignment
Mar 19, 2026Amir Asiaee, Samhita PalCovariate Balancing Heterogeneous Treatment Effects
Department of Biostatistics, Vanderbilt University Medical Center, TN 37203, USA
Conditional Distributional Treatment Effects: Doubly Robust Estimation and Testing
Mar 17, 2026Saksham Jain, Alex LuedtkeHeterogeneous Treatment Effects Covariate Balancing
Department of Statistics, University of Washington, Seattle, USA · Departments of Health Care Policy and Statistics, Harvard University, USA

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Proximal Balancing for Causal Effect Estimation under Unmeasured Confounding

Yonghan Jung

School of Information Sciences University of Illinois Urbana-Champaign

Causal Inferences Latent Confounders Covariate Balancing Latent Variable Impact Analysis

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Estimating causal effects from observational data is central to science and policy, but the effects are not identified when confounders are unmeasured. Proximal causal inference addresses this problem with proxies of the unmeasured confounders. However, existing proxy-based approaches either designate proxy roles and solve an inverse problem, which is ill-posed and hard to estimate with high-dimensional proxies, or use a latent-variable model, which assumes that the learned latent variable matches the hidden confounder and leaves bias when it does not. To address these challenges, we introduce proximal balancing. It carries the classical idea of covariate balancing to confounders that are observed only through proxies: it learns a low-dimensional summary of the covariates and proxies that makes the treatment groups comparable, and then adjusts for this summary. It needs no designated proxy roles, inverse problem, or latent model. We give identification theory, finite-sample guarantees, and a practical algorithm, PROBE. We demonstrate the method on low-dimensional, high-dimensional, and image proxies and on real-world data.

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