Causal Inference

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21 papers in the last four weeks, up 62% on the four weeks before. 0.2% of all new papers.

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Latest papers 222

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EviDAG: Auditable Causal DAG Authoring with Biomedical Literature
Jul 23, 2026Yi-han Sheu, Michael R. Steigman, Yu Zhou +3Data Provenance Evidence-Grounded Reasoning
Massachusetts General Hospital, USA · Harvard University, USA · Northeastern University, USA
Automated Synthesis and Adversarial Validation of Executable Causal Research Pipelines
Jul 23, 2026Irena Girshovitz, Dan Zeltzer, Ran Gilad-BachrachAI-Assisted Scientific Research Scientific Code Generation
School of Biomedical Engineering Tel Aviv University Tel Aviv, Israel · School of Public Health University of California, Berkeley Berkeley, CA, USA
Equilibrium Causal Digital Twins: Validation, Transport, and Identification Limits
Jul 23, 2026Faraz Dadgostari, Neda NazemiCounterfactual Evaluation Structural Causal Models
Department of Mechanical & Industrial Engineering, Montana State University · Gianforte School of Computing, Montana State University
Probabilistic Residual Learning for Online Recommendations
Jul 23, 2026Wenyuan Wang, Yusong Zhao, Zihao Xu +11Residual Learning Recommender Systems
Rutgers University · Piscataway, New Jersey, United States · Meta +4
Equilibrium Causal Games: Separation, Identification, and the Identifiability of Cyclic Latent States
Jul 21, 2026Faraz Dadgostari, Neda NazemiCausal Representation Learning Causal Identifiability
Department of Mechanical & Industrial Engineering, Montana State University · Gianforte School of Computing, Montana State University
Vector Search As Nearest Neighbor Matching: RAG-based Policy Learning in Causal Inference
Jul 20, 2026Masahiro Kato, Taka KatoRetrieval-Augmented Generation Causal Inference
∗The University of Tokyo. · †NP-hard Inc.
Scalable Causal Imitation Learning
Jul 18, 2026Eylam Tagor, Mingxuan Li, Elias BareinboimReinforcement Learning Imitation Learning
Department of Computer Science, Columbia University
Causal Inference for Sequential Settings under Interference and Latent Confounding
Jul 16, 2026Phevos Paschalidis, Constantinos Daskalakis, Devavrat ShahCausal Effect Estimation Ising Model
EECS, LIDS, CSAIL · MIT · EECS, CSAIL +1
Solution of the Hempel's statistical ambiguity problem and Causal AI
Jul 14, 2026Evgenii VityaevCausal Inference Causal Explanation
Sobolev Institute of Mathematics of the SB RAS, Koptuga 4, Novosibirsk, 630090, Russia.
Demonstration of the common dual-channel feature decoupling characteristic of front-door mediation causal inference methods in whole-slice image classification
Jul 14, 2026Zhirui Zhang, Tianhang Nan, Yong Ding +3Representation Disentanglement Histopathology Image Classification
College of Medicine and Biological Information Engineering, Northeastern University, China
Workload-Preserving Differentially Private Synthetic Data for Causal Inference via Maximum-Entropy Calibration
Jul 9, 2026Amir Asiaee, Kaveh AryanCausal Effect Estimation Synthetic Data Generation
Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee, USA · Department of Informatics, King’s College London, London, UK
Causal Optimizer Interaction Calculus: Hidden Geometric Relaxation and Identifiable Interventions
Jul 8, 2026Zavier LiCausal Inference
Xidian University Xi’an, China
CanniUplift: A Holistic Framework for Mitigating Seller and Incentive Cannibalization in E-commerce Uplift Modeling
Jul 6, 2026Zuwang He, Shihao Shu, Yuli Qu +8Causal Effect Estimation E-Commerce
Taobao & Tmall Group of Alibaba Beijing, China · Taobao & Tmall Group of Alibaba Hangzhou, Zhejiang, China
Causal-RetiGraph: Cross-Cohort Retinal Support and Same-Subject Pathway Analysis for Diabetic Retinopathy
Jul 6, 2026Inam Ullah, Imran Razzak, Shoaib JameelCausal Mediation Analysis Diabetic Retinopathy Grading
University of Southampton Southampton, United Kingdom · Mohamed bin Zayed University of Artificial Intelligence United Arab Emirates
Geometric Causal Models
Jul 6, 2026Eli N. Weinstein, David M. BleiCausal Effect Estimation Causal Identifiability
Department of Chemistry, Technical University of Denmark, Kgs. Lyngby, DK. · Departments of Statistics and Computer Science, Columbia University, New York, NY, USA.
Fixed-Confidence Best-Arm Identification for Causal Mediation Analysis
Jul 5, 2026Harsh Shrivastava, Yuta Kawakami, Junpei Komiyama +1Multi-Armed Bandits Causal Effect Estimation
Mohamed bin Zayed University of Artificial Intelligence (MBZUAI), Abu Dhabi, UAE · RIKEN AIP, Tokyo, Japan
Significance-First Splitting: Aligning Treatment Heterogeneity Detection with Honest Estimation
Jul 4, 2026Pantelis Z. Hadjipantelis, Weng Man Chiang, Karthik NageshCausal Effect Estimation Confidence Region Estimation
TripAdvisor, UK
Optimizing Large Language Models for Causality Assessment in Pharmacovigilance: Developing a Performance Metric as Objective for Bayesian Hyperparameter Optimization
Jul 4, 2026Nicole Sonne Heckmann, Arnault-Quentin Vermillet, Søren Norlin Mølgaard +4Bayesian Optimization Large Language Model-Guided Optimization
Department of Drug Design and Pharmacology, University of Copenhagen, Copenhagen, Denmark. · Safety Operations, Novo Nordisk, Søborg, Denmark. · Clinical Development Centre Denmark, Novo Nordisk, Søborg, Denmark. +1
CaSPECT: Discovering Causally Homogeneous Subgroups via Directed Spectral Clustering
Jul 3, 2026Arghya Pratihar, Shinjon Chakraborty, Swagatam DasCausal Effect Estimation Causal Structure Learning
1*Electronics and Communication Sciences Unit, Indian Statistical Institute, India. · 2Indian Statistical Institute, India.
Hierarchical Clustering As a Novel Solution to the Notorious Multicollinearity Problem in Observational Causal Inference
Jun 30, 2026Yufei Wu, Zhiying Gu, Alex Deng +2Causal Identifiability Causal Inference
Airbnb, Inc. San Francisco, California, USA
Doubly Robust Adaptive Conformal Inference for Causal Effects Under Temporal Dependence
Jun 29, 2026Andreas Koukorinis, Ricardo SilvaCausal Effect Estimation Conformal Prediction
Department of Computer Science University College London · Department of Statistical Science University College London
Non-parametric recovery of causal diffusion mechanisms from steady-state observations
Jun 29, 2026Richard Schwank, Mathias DrtonStochastic Differential Equations Causal Identifiability
School of Computation, Information and Technology, Technical University of Munich · 2Munich Center for Machine Learning
Sample-Efficient Learning of Probabilistic Causes for Reachability in Markov Decision Processes with Probabilistic Guarantees
Jun 29, 2026Ryohei Oura, Georgios Fainekos, Hideki Okamoto +1Reachability Analysis Markov Decision Processes
Toyota Research Institute of North America, Toyota Motor North America · Frontier Research Center, Toyota Motor Corporation
Lifted Causal Inference
Jun 26, 2026Malte Luttermann, Tanya Braun, Ralf Möller +1Causal Effect Estimation Efficient Inference
Institute for Humanities-Centered Artificial Intelligence, University of Hamburg, Hamburg, Germany. · German Research Center for Artificial Intelligence (DFKI), Lübeck, Germany. · Data Science Group, University of Münster, Münster, Germany.
The Curse of Multiple Mediators: Hidden Interaction Effects in Activation Patching
Jun 25, 2026Sankaran Vaidyanathan, David Arbour, Aaron Mueller +2Causal Effect Estimation Causal Attribution
University of Massachusetts Amherst · Adobe Research · Boston University
Cross-Head Attention Uplift Network with Inverse Propensity Score under Unobserved Confounding
Jun 25, 2026Haoran Zhang, Chuanpu Li, Yuxin Fu +4Inverse Probability Weighting Causal Effect Estimation
Center for Applied Statistics and School of Statistics, Renmin University of China, Beijing, China · Alibaba Group, Beijing, China
A Causal Foundation Model for Structure and Outcome Prediction
Jun 25, 2026Max Zhu, Martino Mansoldo, Ching-Hao Wang +1Causal Foundation Models Causal Structure Learning
University of Cambridge, United Kingdom · GSK.ai.
Infinitesimal Causality
Jun 23, 2026Sridhar MahadevanStructural Causal Models Causal Inference
Adobe Research and University of Massachusetts, Amherst
RetiSEM: Generalising Causal Models for Fragmented Biomedical Data
Jun 23, 2026Inam Ullah, Imran Razzak, Shoaib JameelLatent Variable Models Structural Causal Models
University of Southampton, Southampton, SO18 1PB, Hampshir, United Kingdom · Mohamed bin Zayed University of Artificial Intelligence, United Arab Emirates
An Introduction to Causal Reinforcement Learning
Jun 23, 2026Elias Bareinboim, Junzhe Zhang, Sanghack LeeReinforcement Learning Causal Inference
Department of Computer Science Columbia University New York, USA · Graduate School of Data Science Seoul National University Seoul, Republic of Korea

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EviDAG: Auditable Causal DAG Authoring with Biomedical Literature

Yi-han Sheu, Michael R. Steigman, Yu Zhou, Bo Wang+2

Massachusetts General Hospital, USA · Harvard University, USA · Northeastern University, USA

Data Provenance Evidence-Grounded Reasoning Causal Structure Learning Causal Inference

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Constructing causal directed acyclic graphs (DAGs) is a core step in biomedical causal analysis, yet it remains a largely manual process. Analysts must connect study variables to prior literature, evaluate uncertain causal claims, and preserve sufficient provenance for expert review. We present EviDAG, a browser-based system for authoring causal DAGs as auditable, evidence-linked artifacts from biomedical literature. Given free-text descriptions of study concepts, EviDAG creates a reproducible literature snapshot, uses an LLM-based reasoning module to generate structured pairwise causal judgments, links literature-supported judgments to verbatim evidence excerpts, and assembles the judgments into a constraint-checked graph. Each proposed edge includes confidence estimates, provenance, and a reviewable rationale. The interface supports study specification, progress monitoring, evidence review, graph comparison, adjustment-set computation, and export. In evaluations against both compact benchmark DAGs and reference DAGs derived from published literature, EviDAG achieves high edge recall on the literature-based cohort while retaining verifiable evidence trails absent from LLM-only baselines. EviDAG thus reduces the burden of causal DAG curation while making the resulting assumptions auditable, supporting the design, analysis, and interpretation of biomedical studies.

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