False Discovery Rate

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Decoupled Causal Discovery
Sep 20, 2026Zhengkang Guan, Fei Wu, Kun KuangCausal Foundation Models False Discovery Rate
Resolution-Aware Experimental Design under Partial Identifiability
Sep 3, 2026Sofianos Panagiotis FotiasBayesian Experimental Design Identifiability
ARM: Detector-Agnostic Changepoint Attribution with Finite-Sample Error Control
Aug 3, 2026Chenchen Peng, Mixia Wu, Qijing Yan +2Change-Point Detection False Discovery Rate
Model-Agnostic FDR Control via Group Gaussian Mirror and Permutation SHAP
Aug 2, 2026Jiaan Han, Junxiao Chen, Yanzhe FuFeature Importance False Discovery Rate
High-Order Markov Blanket Discovery via a k-Order Relaxation of the Faithfulness Assumption
Jul 29, 2026Loong Kuan Lee, Ragavi Krishnamoorthy, Nico PiatkowskiBayesian Networks Markov
Robust Conformalized Selection with Noisy Responses
Jul 25, 2026Chengyao Yu, Hongxin Wei, Bingyi JingFalse Discovery Rate Conformal Calibration
Panache: One-Pass Motif Discovery at Every Window Length
Jul 20, 2026Tej Sanibh RanadeMotif Data Mining
DP-BOA: Dirichlet-Process Birth-or-Assign for On-the-Fly Category Discovery
Jul 15, 2026Peiyan Gu, Zixin Teng, Xuming HeGeneralized Category Discovery Mixture Models
The Benjamini--Hochberg Procedure Can Fail to Control the FDR for Correlated Two-Sided Gaussian Tests
Jul 13, 2026Edgar DobribanFalse Discovery Rate Hypothesis
AdaStop: Cost-Aware Early Stopping for DNN Test Selection
Jul 6, 2026Bonan Shen, Wei-Jung Huang, Xin Liu +2Early Stopping Noisy Labels
MDL Meets Latent Confounders: LNML-based Causal Discovery
Jul 5, 2026Zhongyi Que, Shin Matsushima, Kenji YamanishiCausal Foundation Models Latent Confounders
Efficient Discovery of Conditional Dependencies with Desbordante
Jul 4, 2026Ivan Kozhukov, Dmitry Fedoseev, Maksim Emelyanov +4Data Mining False Discovery Rate
Conditional Inference Trees and Forests for Feature Selection
Jul 1, 2026Robert Milletich, Justin Downes, Steve Goley +1Feature Selection Decision Trees
When to Truncate a Feature Ranking: A Residual-Overlap Stopping Rule for Subset Selection
Jun 30, 2026Jesus S. Aguilar-RuizFeature Selection False Discovery Rate
Where Is My Physics Wrong? Localized and Identifiable Discovery of Model Discrepancy
Jun 22, 2026Yifan WangDiscrepancy Mismatch
Null-Calibrated Conformal Selection via Target-Membership Scores
Jun 21, 2026Seungjin ChoiNonconformity Scores Conformal Calibration
Finite Resources False Discovery Rate Control in Structured Hypothesis Spaces
Jun 13, 2026Binyamin Perets, Shie MannorFalse Discovery Rate Two-Sample Testing
Discovery under Hypothesis Redundancy: A Geometric Theory of Discovery Bottlenecks
Jun 12, 2026Li Xia, Baoxun WangHypothesis Generation Efficient Exploration
Conformal calibration and look-elsewhere effect in anomaly detection for new-physics searches
Jun 11, 2026Jack Y. Araz, Michael SpannowskyAnomaly Score High Energy Physics
StatefulDiscovery: Evidence-Calibrated Claim Formation in Open-Ended Scientific Discovery
Jun 10, 2026Jiayao Chen, Shi Liu, Linyi YangScientific Discovery False Discovery Rate
A machine-learning-assisted progressive digit-randomness screening framework for detecting non-random patterns in raw numerical research data
Jun 5, 2026Zhuphua CaoFalse Discovery Rate Random
Bias in Filter Feature Selection Evaluation: A Meta-Analysis of Datasets, Baselines, and Experimental Design Choices
Jun 5, 2026Malick Ebiele, Malika Bendechache, Rob BrennanFeature Selection False Discovery Rate
Knockoffs-based False Discovery Rate Control and Simplification for Deep Neural Networks
Jun 3, 2026Wenyu Liao, Yiqing Shi, Fang XieFalse Discovery Rate Screening
A Distribution-Free Framework for Rewrite-Based Human-text Detection via Knockoff Filtering
May 29, 2026Yi LiuMachine-Generated Text Detection False Discovery Rate
Fraud Type Decomposition and the Observation-Mechanism Taxonomy:Class-Specific Detection Limits in Payment Networks
May 29, 2026Gaurav DhamaFraud Detection Payment Networks
Data-driven sparse identification of governing PDEs via knockoff filters and multi-criteria trade-offs
May 26, 2026Pongpisit Thanasutives, Naichang Ke, Yoshinobu KawaharaFalse Discovery Rate Partial Differential Equations
Worse than Random: The Importance of a Baseline for Unsupervised Feature Selection
May 21, 2026Muhammad Rajabinasab, Michael E. Houle, Oussama Chelly +1Feature Selection Unsupervised
SilIF: Silhouette-Augmented Isolation Forest for Unsupervised Transaction Fraud Detection
May 21, 2026Venkatakrishnan GopalakrishnanFraud Detection Reconstruction-Based Anomaly Detection
Robust Personalized Recommendation under Hidden Confounding in MNAR
May 20, 2026Zongyu Li, Wanting Su, Tianyu XiaLatent Confounders Selection Bias
Everywhere Valid Bounds on False Discovery Proportions in Conformal Inference
May 20, 2026Ziang Song, Ying Jin, Emmanuel J. CandèsFalse Discovery Rate Conformal Inference
Controlling False Discovery in Arbitrarily Structured Hypothesis Spaces via Reproducing Kernels
May 17, 2026Binyamin Perets, Shie MannorFalse Discovery Rate Kernel Hilbert Spaces
Conformal Anomaly Detection in Python: Moving Beyond Heuristic Thresholds with 'nonconform'
May 13, 2026Oliver Hennhöfer, Maximilian Kirsch, Christine PreisachAnomaly Score Anomaly Detection
A Regret Perspective on Online Multiple Testing
May 13, 2026Qingyang Hao, Kongchang Zhou, Fang Kong +1False Discovery Rate Two-Sample Testing
Many Needles in a Haystack: Active Hit Discovery for Perturbation Experiments
May 11, 2026Andrea Rubbi, Arpit Merchant, Samuel Ogden +4Bayesian Experimental Design Bayesian Optimization
Decentralized Conformal Novelty Detection via Quantized Model Exchange
May 7, 2026Kyle Loh, Yu XiangFalse Discovery Rate Privacy-Preserving Framework
Fourier Feature Methods for Nonlinear Causal Discovery: FFML Scoring, TRFF Scoring, and FFCI Testing in Mixed Data
May 7, 2026Joseph D. RamseyCausal Foundation Models Conditional Independence
Pi-Change: A Prior-Informed Multiple Change Point Detection Algorithm
May 1, 2026Jonathon Jacobs, Shanshan ChenChange-Point Detection Change
Beyond Fixed False Discovery Rates: Post-Hoc Conformal Selection with E-Variables
Apr 13, 2026Meiyi Zhu, Osvaldo SimeoneFalse Discovery Rate Conformal Calibration
ROOFS: RObust biOmarker Feature Selection
Jan 8, 2026Anastasiia Bakhmach, Paul Dufossé, Simon Charpigny +4Feature Selection Biomarker
Structural Enforcement of Statistical Rigor in AI-Driven Discovery: A Functional Architecture
Nov 10, 2025Karen SargsyanFalse Discovery Rate Two-Sample Testing
Multiple Testing of Linear Forms for Noisy Matrix Completion
Dec 1, 2023Wanteng Ma, Lilun Du, Dong Xia +1Tensor Completion False Discovery Rate

Sep 20, 2026 · cs.LGJ/K move · Enter open · S save

Decoupled Causal Discovery

Zhengkang Guan, Fei Wu, Kun Kuang

Causal discovery from observational data is a fundamental yet challenging task in scientific research. While existing approaches are primarily based on conditional independence tests, structure scores, or restrictive functional assumptions, we propose Decoupled Causal Discovery (DCD), a novel decoupling-based perspective that does not rely on these methodologies. DCD directly identifies the Markov boundary (MB) by decoupling non-target variables via weighting functions, such that only variables within the MB preserve dependence with the target under the decoupled distribution. Building on this, DCD iteratively constructs the Completed Partially Directed Acyclic Graph (CPDAG) by exploiting structural asymmetries within the MBs. We establish the theoretical identifiability, soundness, and completeness of DCD. Empirical evaluations demonstrate that DCD achieves strong performance, particularly excelling in challenging noise regimes.

Causal Foundation Models False Discovery Rate Identifiability

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