Causal Discovery

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17 papers in the last four weeks, up 183% on the four weeks before. 0.2% of all new papers.

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Iterative Causal Discovery: Per-Edge Impossibility Certificates, Tier-Aware Oracle Queries, and the $1+K$ Lower Bound
May 26, 2026Eichi UeharaBayesian Networks Causal Discovery
aflo, Inc., Tokyo, Japan
CausaLab: A Scalable Environment for Interactive Causal Discovery Toward AI Scientists
May 25, 2026Junlin Yang, Dylan Zhang, Xiangchen Song +7Structural Causal Models LLM Agent Evaluation
Tsinghua University · University of Illinois Urbana-Champaign · Carnegie Mellon University +2
Evolving Causal Regulatory Networks (ECR-Net)
May 24, 2026Govind Vallabhasseri Binish, Abdhul Ahadh, Rano Roy Kavanal +1Structural Causal Models Causal Discovery
From Activation to Causality: Discovery of Causal Visual Representations in the Human Brain
May 22, 2026Yuval Golbari, Navve Wasserman, Matias Cosarinsky +5Causal Representation Learning Causal Discovery
Weizmann Institute of Science · Massachusetts Institute of Technology
Leveraging Foundation Models for Causal Generative Modeling
May 22, 2026Aneesh Komanduri, Xintao WuCausal Counterfactual Generation Image Generation
University of Arkansas Fayetteville, Arkansas, USA
Causal Discovery in Structural VAR Models Under Equal Noise Variance
May 21, 2026SeyedSina Seyedi HasanAbadi, Fahimeh Arab, Erfan Nozari +1Time-Series Causal Discovery Causal Discovery
Bourns College of Engineering, University of California, Riverside · University of California, San Francisco · Department of Mathematics and Statistics, Boston University
Local Covariate Selection for Average Causal Effect Estimation without Pretreatment and Causal Sufficiency Assumptions
May 20, 2026Zeyu Liu, Zheng Li, Feng Xie +3Causal Effect Estimation Causal Discovery
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
Score-Based Causal Discovery of Latent Variable Causal Models
May 19, 2026Ignavier Ng, Xinshuai Dong, Haoyue Dai +3Latent Variable Models Structural Causal Models
Carnegie Mellon University · University of California, San Diego · Mohamed bin Zayed University of Artificial Intelligence
A Unified Framework for Structure-Aware Clustering and Heterogeneous Causal Graph Learning
May 19, 2026Honglin Du, Muxuan Liang, Xiang ZhongStructural Causal Models Clustering
Department of Industrial and Systems Engineering, University of Florida, Gainesville, FL 32611, USA · Department of Biostatistics, MD Anderson Cancer Center, Houston, TX 77030, USA
TriOpt: A Scalable Algorithm for Linear Causal Discovery
May 17, 2026Rafat Ashraf Joy, Elena ZhelevaStructural Causal Models Causal Discovery
University of Illinois Chicago
SCOUT: Cyclic Causal Discovery Under Soft Interventions with Unknown Targets
May 15, 2026Alpar Turkoglu, Muralikrishnna G. Sethuraman, Faramarz FekriCausal Discovery Causal Intervention
School of Electrical and Computer Engineering, Georgia Institute of Technology.
PACER: Acyclic Causal Discovery from Large-Scale Interventional Data
May 14, 2026Ramon Viñas Torné, Sílvia Fàbregas Salazar, Soyon Park +4Causal Discovery Causal Structure Learning
Swiss Federal Technology Institute of Lausanne (EPFL), Switzerland · ETH Zurich, Zurich, Switzerland · Cornell University, USA
Causal Learning with the Invariance Principle
May 13, 2026Francesco Montagna, Francesco LocatelloStructural Causal Models Causal Discovery
Institute of Science and Technology Austria
A Recursive Decomposition Framework for Causal Structure Learning in the Presence of Latent Variables
May 11, 2026Zheng Li, Feng Xie, Shenglan Nie +3Causal Discovery Causal Structure Learning
Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China · Department of Applied Statistics, Beijing Technology and Business University, Beijing, China · College of Computer Science and Artificial Intelligence, Fudan University, Shanghai, China
Coarsening Linear Non-Gaussian Causal Models with Cycles
May 11, 2026Francisco Madaleno, Francisco C Pereira, Alex MarkhamStructural Causal Models Causal Discovery
Department of Technology, Management and Economics, Technical University of Denmark · Department of Mathematical Sciences,11 University of Copenhagen
Intervention-Based Time Series Causal Discovery via Simulator-Generated Interventional Distributions
May 11, 2026Tsuyoshi OkitaTime-Series Causal Discovery Causal Discovery
Kyushu Institute of Technology
Perception Without Engagement: Dissecting the Causal Discovery Deficit in LMMs
May 10, 2026Jiafeng Liang, Zhihao Zhu, Zihan Zhang +7Causal Discovery Multimodal Large Language Models
Harbin Institute of Technology · Harvard University · Peking University +2
Prediction Bottlenecks Don't Discover Causal Structure (But Here's What They Actually Do)
May 9, 2026Ankit Hemant Lade, Sai Krishna Jasti, Indar Kumar +1Time-Series Causal Discovery Causal Discovery
Physical Simulators as Do-Operators: Causal Discovery under Latent Confounders for AI-for-Science
May 8, 2026Tsuyoshi OkitaAI for Science Causal Discovery
Kyushu Institute of Technology
PerCaM-Health: Personalized Dynamic Causal Graphs for Healthcare Reasoning
May 8, 2026Elahe Khatibi, Ziyu Wang, Saba A. Farahani +4Structural Causal Models Healthcare
University of California, Irvine · Washington University in St. Louis
Arrow: A Foundation Model for Causal Discovery
May 8, 2026Ryan Thompson, He Zhao, Daniel M. Steinberg +1Causal Foundation Models Tabular Foundation Models
University of Technology Sydney · CSIRO’s Data61
A Topological Sorting Criterion for Random Causal Directed Acyclic Graphs
May 7, 2026Alexander G. Reisach, Antoine Chambaz, Gilles Blanchard +1Causal Discovery Causal Identifiability
Laboratoire de Mathématiques d’Orsay Université Paris-Saclay 91400 Orsay, France · CNRS, MAP5 Université Paris Cité F-75006 Paris, France · Department of Mathematical Sciences and Pioneer Centre for Artificial Intelligence, University of Copenhagen, Denmark
Fourier Feature Methods for Nonlinear Causal Discovery: FFML Scoring, TRFF Scoring, and FFCI Testing in Mixed Data
May 7, 2026Joseph D. RamseyFourier Feature Embeddings Causal Discovery
Department of Philosophy Carnegie Mellon University Pittsburgh, PA 15213
Relaxed Sparsest-Permutation Formulation for Causal Discovery at Scale
May 7, 2026Sunmin Oh, Sang-Yun Oh, Gunwoong ParkStructural Causal Models Causal Discovery
Department of Statistics, Seoul National University, South Korea · Department of Statistics and Applied Probability, University of California Santa Barbara, CA, USA · Scientific Data Division, Lawrence Berkeley National Laboratory, CA, USA +2
Creative Robot Tool Use by Counterfactual Reasoning
May 6, 2026M. Tuluhan Akbulut, Varun Satheesh, Ahmed Jaafar +5Causal Discovery Robotic Tool Use
Brown University, Providence, RI, USA
Order-based Rehearsal Learning
May 6, 2026Yu-Xuan Tao, Tian-Zuo Wang, Zhi-Hua ZhouCausal Discovery Causal Structure Learning
National Key Laboratory for Novel Software Technology, Nanjing University, China · School of Artifcial Intelligence, Nanjing University, China
When Does Gene Regulatory Network Inference Break? A Controlled Diagnostic Study of Causal and Correlational Methods on Single-Cell Data
May 6, 2026Miguel Fernandez-de-Retana, Ruben Sanchez-Corcuera, Unai Zulaika +2Causal Discovery Gene Regulatory Network Inference
Faculty of Engineering University of Deusto Bilbao, Spain
PAIR-CI: Calibrated Conditional Independence Testing for Causal Discovery with Incomplete Data
May 6, 2026Thomas S. Robinson, Ranjit LallIncomplete Data Imputation Causal Discovery
Department of Methodology London School of Economics and Political Science London, UK · Department of Politics and International Relations University of OxfordMay Oxford, UK
Causal discovery under mean independence and linearity
May 6, 2026Geert Mesters, Alvaro Ribot, Anna Seigal +1Structural Causal Models Causal Discovery
Partial Effective Information Decomposition for Synergistic Causality
May 5, 2026Mingzhe Yang, Shuo Wang, Jiang ZhangCausal Discovery Causal Intervention
School of Systems Science Beijing Normal University

May 26, 2026, stat.MLJ/K move · Enter open · S save

Iterative Causal Discovery: Per-Edge Impossibility Certificates, Tier-Aware Oracle Queries, and the $1+K$ Lower Bound

Eichi Uehara

aflo, Inc., Tokyo, Japan

Bayesian Networks Causal Discovery Causal Structure Learning Causal Identifiability

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Causal-discovery algorithms return a directed graph, yet provide no principled means of distinguishing edge directions identified by the data from those assigned without an identifying assumption. Under the standard Markov and faithfulness conditions, the observational distribution identifies only a Markov equivalence class; orientations within that class are not determined by the joint distribution and cannot be recovered from additional samples alone, but require either a functional restriction or an intervention. We introduce a protocol for observational causal discovery on continuous data that attaches to each candidate edge a discrete impossibility certificate: a RESOLVED code records the identifiability theorem under which the direction was committed, while an IMPOSSIBLE code records the failure mode together with the specific question a domain expert must answer to resolve it. The bivariate cascade is extended with five gated identifiability tiers LSNM, IGCI, Stein, MDL, and PEIT that abstain when their precondition test rejects. Two oracle primitives, the meta-hub query and the node-children query, jointly establish an upper bound of

1+K

expert interactions sufficient to recover any DAG, where

K

denotes the number of non-leaf vertices. Under an ideal-oracle assumption, the bound is met exactly on the asia, sachs, child, and alarm benchmarks.

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Latest papers 148