Selection Bias

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Overview

Selection bias arises when the people, examples, or outcomes observed differ systematically from the population a study or model is meant to represent. Researchers use methods such as inverse-probability weighting, post-stratification, robust optimization, and Heckman-style models to correct for selection, while causal inference asks when effects remain identifiable. Work also examines how biased samples affect medical prediction, federated learning, surveys, and model evaluation, using tools such as negative controls, calibration checks, and controlled exploration. A central challenge is that weighting can address measured differences but may fail when selection depends on unobserved factors, so reliable correction often requires explicit assumptions, external information, or better data collection.

Latest papers

CardsList

44 papers8 papers in the last four weeks, level with the four weeks before. 0.1% of all new papers.

  1. Confidence Filtering in Video-Based Gait Assessment: Measurement Error versus Selection Shift

    Oct 29, 2026David Regev, Eran Beeri BamaniSelection BiasGait Analysis

    Department of Information Science and Applied Artificial Intelligence, Bar-Ilan University, Ramat Gan, Israel.

  2. Sharp Bounds on the Winner's Bias under Independent and Nonnegatively Correlated Noise

    Oct 22, 2026Ivan Viakhirev, Kirill Borodin, Grach MkrtchianCross-ValidationSelection Bias

    lab260 (lab260.ru) · BitmanagerAI (bitmanager.ai)

  3. Wet-Biased Data Pipelines Mislead the Training and Evaluation of Generative Precipitation Nowcasting

    Oct 19, 2026Christoph Metzl, Stefan Kapsreiter, Kianusch Vahid Yousefnia +4Precipitation NowcastingGenerative AI Evaluation

    Deutsches Zentrum für Luft- und Raumfahrt, Institut für Physik der Atmosphäre, Oberpfaffenhofen, Germany · Wismar University of Applied Sciences, Wismar, Germany · Arpae Emilia-Romagna, Hydro-Meteo-Climate Service (SIMC), Bologna, Italy +1

  4. Reliable Estimation of Differential Privacy under Selection Bias

    Oct 19, 2026Valentin Dorseuil, Meenatchi Sundaram Muthu Selva Annamalai, Jamal Atif +2Privacy AuditingDifferential Privacy

    DI-ENS · UCL · CMAP +1

  5. INCONVENIENT: A Benchmark to Assess LLM Generalization Capabilities from Convenience Samples

    Oct 15, 2026Sara Rezaeimanesh, Chad Klochko, Kundan Thind +1Distribution Shift RobustnessTabular ML

    Michigan State University, East Lansing, MI, USA · Henry Ford Health, Detroit, MI, USA

  6. One Bias Score, Two Factors: How Mitigations Affect Option Bias in Multiple-Choice Evaluation

    Oct 14, 2026Hyunjong Ok, Minjae Park, Jaeho LeeInput Order Sensitivity in Language ModelsLanguage Model Bias Evaluation

    Pohang University of Science and Technology (POSTECH)

  7. Causal Discovery Under Hard Selection Bias: A New Robust Score-Matching Approach

    Oct 13, 2026Yiwen Qiu, Francesco Montagna, Shimeng Huang +1Causal DiscoverySelection Bias

    IST Austria, Am Campus 1, Klosterneuburg, 3400

  8. Shared and structured inputs undermine collective random choice by reasoning AI agents

    Oct 7, 2026Takahiro Ezaki, Naoto Imura, Katsuhiro NishinariAI Agent EvaluationSelection Bias

    Research Center for Advanced Science and Technology, The University of Tokyo 4-6-1 Komaba, Meguro-ku, Tokyo 153-8904, Japan · Department of Aeronautics and Astronautics, School of Engineering, The University of Tokyo 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan

  9. EasyClassifier: Honest, Reproducible Machine-Learning Classification for Researchers Who Do Not Program

    Oct 3, 2026Ahmad B. A. Hassanat, Ghada A. AltarawnehML ReproducibilityClassification

    Faculty of Information Technology, Mutah University, Karak, Jordan · Department of Accounting, Mutah University, Karak, Jordan

  10. Oracle headroom without signal: null-calibrated evaluation of candidate selection for thermal heart rate estimation

    Oct 1, 2026Mohammad Rakibur Rahman, Nhi Nguyen, Sasan Sharifipour +4Heart Rate EstimationSelection Bias

    Center for Machine Vision and Signal Analysis (CMVS), University of Oulu, Finland

  11. False Floors: LLM Safety Routing Evaluations Break Under Distribution Shift

    Oct 1, 2026Amit Singh Bhatti, Vishal VaddinaLLM Safety BenchmarksDistribution Shift Robustness

    Quantiphi Analytics

  12. Learning-Enabled Estimation: Tight Characterizations under Sample Selection Biases

    Sep 29, 2026Vikram Kher, Jane H. Lee, Anay Mehrotra +1Selection Bias

    Yale University · Stanford University

  13. Oracle Gaps in Reliability Coverage: Sampling Noise or Policy Specialization?

    Sep 26, 2026Mert Onur Cakiroglu, Mehmet Dalkilic, Hasan KurbanLLM ReliabilitySelection Bias

    Luddy School of Informatics, Computing, and Engineering, Indiana University Bloomington, Bloomington, IN, USA · College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar

  14. When Edit Localization Amplifies Relative Selection Bias: Gradient Geometry, Target Mismatch, and Importance Weighting

    Sep 12, 2026Shengwei Zhang, Haoda Dai, Yifei Li +1Selection BiasImportance Weighting

    University of Pennsylvania

  15. Loading history and window geometry bound compact-state slip ranking during granular shear startup

    Sep 9, 2026Ruixin Zhou, Boliang YuMaterials ScienceTime Series Forecasting

    School of Physics and Astronomy, Shanghai Jiao Tong University, Shanghai 200240, China

  16. Semi-Supervised Learning under Spatially Biased Sampling

    Sep 7, 2026Bright Wiredu Nuakoh, Francky Fouedjio, Stephen Bradshaw +4Distribution ShiftSemi-Supervised Learning

    African Institute for Mathematical Sciences (AIMS), 17 KN 16 Ave, Kigali, Rwanda · Mathematical Application & Data Analytics Group, School of Science, Edith Cowan University, 270 Joondalup Drive, Joondalup, WA 6027, Australia · Data & Analytics, Rio Tinto, 152-158 St Georges Terrace, Perth, WA 6000, Australia +2

  17. A survey detection channel overrides the pixels in an astronomical foundation model, and biases tomographic mean redshifts

    Aug 23, 2026Ihor KendiukhovSelection BiasAI-Assisted Scientific Research

    Independent researcher

  18. Sampling Luck Masquerades as Allocation Gain: Auditing Test-Time Budget Allocation for Neural Combinatorial Optimization

    Aug 13, 2026Jinhyung BaeAlgorithmic AuditingInference-Time Compute Allocation

    Department of Industrial and Management Engineering and the AI Data Convergence program, Hankuk University of Foreign Studies, Yongin, Republic of Korea

  19. Automatic Statistical Test for Rationally Expressible Algorithms by Selective Inference, with Applications to Feature Selection

    Aug 5, 2026Teruyuki Katsuoka, Tomohiro Shiraishi, Shuichi Nishino +1Selective InferenceSelection Bias

    Nagoya University · RIKEN

  20. When May a Model Replace the Experiment? Audits, Licenses, and the Price of Trust in Surrogate-Driven Design

    Aug 2, 2026Shuangxiu, Ma, Wenhe +1Surrogate ModelingSurrogate-Assisted Optimization

    Max · William G. Lowrie Department of Chemical and Biomolecular Engineering, The Ohio State University, Columbus, Ohio 43210, United States · Zachary +1

  21. Selection Shapes the Boundary: A Replication of Monotonicity and Label Agreement in Unselected NLI Populations

    Jul 21, 2026Haram ChoiAnnotator DisagreementSelection Bias

    University of Bremen

  22. The Curvature Shadow: An Apparent Failure of Maximum-Entropy Equilibrium Selection is a Removable Artifact

    Jul 20, 2026Luis LealNash EquilibriumImperfect-Information Games

  23. K-ABENA: K-Adaptive Backpropagation with Error-based N-exclusion Algorithm : (Compensated Loss-Based Sample Exclusion with Unbiased Gradient Estimation)

    Jul 7, 2026Jean-Francois BonbhelBackpropagationNeural Network Optimization

    NeuroSoft IA, Qu´ebec City, Canada · YekoElite University, Brazzaville, Republic of Congo

  24. Heckman-Corrected Epistemic Uncertainty: Selection on Unobservables Defeats Importance Weighting

    Jul 7, 2026Gunner Levi HoweSelection BiasUncertainty Calibration

    Independent Researcher

  25. Identifying Structural Biases from Causal Mechanism Shifts

    Jun 17, 2026Praharsh Nanavati, Jilles Vreeken, David KaltenpothStructural Causal ModelsCausal Discovery

    CISPA Helmholtz Center for Information Security

  26. The Illusion of Improvement: Reject Inference Strategies in Credit Scoring

    Jun 16, 2026Bruno Scarone, Ricardo Baeza-YatesCredit ScoringSelection Bias

    Northeastern University, Boston, MA, USA · KTH Royal Institute of Technology, Stockholm, Sweden

  27. Beyond the Training Distribution: Evaluating Predictions Under Distribution Shift and Selection Bias

    Jun 12, 2026Annie Ulichney, Amanda CostonDouble MLDistribution Shift Robustness

    Department of Statistics, University of California, Berkeley

  28. When Sample Selection Bias Precipitates Model Collapse

    Jun 11, 2026Xinbao Qiao, Xianglong Du, Wei Liu +4Synthetic Data PretrainingModel Collapse

    National University of Singapore (Chongqing) Research Institute · Chongqing Key Laboratory of Trusted Perception and Interaction Technology for Intelligent and Connected Vehicles, Chongqing, China · The Chinese University of Hong Kong +4

  29. When Can You Trust Your Synthetic Users? Diagnostics and Corrections for LLM Consumer Panels

    Jun 3, 2026Robson Tigre, Hugo Gobato SoutoSynthetic Data EvaluationUser Simulation

    Recast · Dell

  30. A Practical Upper Bound on Selection Bias Effects in Medical Prediction Models

    May 30, 2026Kara Liu, Maggie Wang, Russ B. AltmanOOD GeneralizationSelection Bias

    Stanford University · Stanford, California, USA