Causal Inference

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Causal inference asks what would change if a treatment, policy, or other factor were different, rather than merely measuring which variables move together. Researchers use randomized and quasi-experiments, causal graphs with adjustment, instrumental variables and proxies, and semiparametric or machine-learning estimators. Causal discovery from observational, time-series, and network data seeks structures that support these analyses, while counterfactual models help evaluate individual outcomes and policies. Results depend on assumptions—especially about hidden confounding, interference, and transport across populations—so sensitivity analysis, partial-identification bounds, and credible evaluation remain central challenges as generative and foundation models are explored.

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318 papers34 papers in the last four weeks, up 13% on the four weeks before. 0.2% of all new papers.

Dual Causal Inference: Integrating Backdoor Adjustment and Instrumental Variable Learning for Medical VQA
Apr 22, 2026Zibo Xu, Qiang Li, Ke Lu +3Multimodal Robustness Medical VQA
School of Microelectronics, Tianjin University, Tianjin 300072, China · Department of Orthopedics, Affiliated Kunshan Hospital of Jiangsu University, Jiangsu 215300, China · Department of Clinical Laboratory, The Third Central Hospital of Tianjin, Tianjin 300170, China +1
Physics-Informed Causal MDPs for Sequential Constraint Repair in Engineering Simulation Pipelines
Apr 20, 2026Chuhan QiaoMarkov Decision Processes Reinforcement Learning
Beijing JiaoTong University
Implicit Causal Graph Construction in Text via Chain Discovery
Apr 20, 2026Liesbeth Allein, Marie-Francine MoensCausal Discovery Causal Reasoning in Language Models
Department of Computer Science, KU Leuven, Belgium · Department of Electronics and Information Systems, Ghent University, Belgium
LLM-Extracted Covariates for Clinical Causal Inference: Rethinking Integration Strategies
Apr 18, 2026Lei Liu, Jialin Chen, Kathy MacropolCausal Effect Estimation Causal Inference
Department of Computer Science and Mathematics Arcadia University Glenside, PA, USA · Department of Computer Science Yale University New Haven, CT, USA
Generative Augmented Inference
Apr 16, 2026Cheng Lu, Mengxin Wang, Dennis J. Zhang +1LLM-Assisted Annotation Causal Inference
Olin Business School, Washington University in St. Louis, USA · Naveen Jindal School of Management, University of Texas at Dallas, USA · W. P. Carey School of Business, Arizona State University, USA
Counterfactual Peptide Editing for Causal TCR--pMHC Binding Inference
Apr 14, 2026Sanjar Khudoyberdiev, Arman BekovCounterfactual Data Augmentation Causal Inference
Causal Diffusion Models for Counterfactual Outcome Distributions in Longitudinal Data
Apr 14, 2026Farbod Alinezhad, Jianfei Cao, Gary J. Young +1Counterfactual Prediction Causal Inference
Analysis Group · Department of Economics, Northeastern University · Center for Health Policy and Healthcare Research, Northeastern University +1
Observing the unobserved confounding through its effects: toward randomized trial-like estimates from real-world survival data
Apr 13, 2026Vasiliki Stoumpou, Dimitris Bertsimas, Samuel Singer +1Causal Inference with Unobserved Confounding Causal Effect Estimation
Operations Research Center, Massachusetts Institute of Technology, Cambridge, MA, USA · Sloan School of Management, Massachusetts Institute of Technology, Cambridge, MA, USA · Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA +1
Identifying Causal Effects Using a Single Proxy Variable
Apr 10, 2026Silvan Vollmer, Niklas Pfister, Sebastian WeichwaldCausal Effect Estimation Causal Inference with Unobserved Confounding
Department of Mathematical Sciences University of Copenhagen Copenhagen, Denmark · Lakera AI Zurich, Switzerland
A Closer Look at the Application of Causal Inference in Graph Representation Learning
Apr 10, 2026Hang Gao, Kunyu Li, Huang Hong +2Graph Representation Learning Causal Inference
Institute of Software Chinese Academy of Science
From Plausible to Causal: Counterfactual Semantics for Policy Evaluation in Simulated Online Communities
Apr 5, 2026Agam Goyal, Yian Wang, Eshwar Chandrasekharan +1Counterfactual Evaluation Large Language Model-Based Social Simulation
University of Illinois Urbana-Champaign, Urbana, Illinois, USA
Transfer Learning for Meta-analysis Under Covariate Shift
Apr 3, 2026Zilong Wang, Ali Abdeen, Turgay AyerCausal Inference Individualized Treatment Effect Estimation
Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA
Decomposing Discrimination: Causal Mediation Analysis for AI-Driven Credit Decisions
Mar 29, 2026Duraimurugan RajamanickamFinancial Services Causal Effect Estimation
VP, Artificial Intelligence, Hudson Valley Credit Union, Poughkeepsie, NY · PhD Candidate, Causal Machine Learning, University of Arkansas at Little Rock
A Causal Framework for Evaluating ICU Discharge Strategies
Mar 26, 2026Sagar Nagaraj Simha, Juliette Ortholand, Dave Dongelmans +4Healthcare Sequential Decision Making
Department of Medical Informatics, Amsterdam UMC, University of Amsterdam, NL · Department of Intensive Care Medicine, Amsterdam UMC, University of Amsterdam, NL · Department of Intensive Care, Elisabeth-TweeSteden Hospital, Tilburg, NL +3
CIV-DG: Conditional Instrumental Variables for Domain Generalization in Medical Imaging
Mar 26, 2026Shaojin Bai, Yuting Su, Weizhi NieDomain Generalization Cross-Domain Generalization
School of Electrical and Information Engineering, Tianjin University, Tianjin, China
Causal Transfer in Medical Image Analysis
Mar 25, 2026Mohammed M. Abdelsamea, Daniel Tweneboah Anyimadu, Tasneem Selim +4Medical Image Analysis Cross-Domain Transfer Learning
Department of Computer Science, University of Exeter, Exeter, United Kingdom · Department of Mathematics and Computer Science, Faculty of Science, Alexandria University, Alexandria, Egypt · Hawkes Institute, Department of Computer Science, University College London, London, United Kingdom
Wasserstein Parallel Transport for Predicting the Dynamics of Statistical Systems
Mar 24, 2026Tristan Luca Saidi, Gonzalo Mena, Larry Wasserman +1Batch Effect Correction Causal Counterfactual Generation
Department of Statistics and Data Science, Carnegie Mellon University · Machine Learning Department, Carnegie Mellon University · Department of Economics, Emory University
SemEval-2026 Task 12: Abductive Event Reasoning: Towards Real-World Event Causal Inference for Large Language Models
Mar 23, 2026Pengfei Cao, Mingxuan Yang, Yubo Chen +4Abductive Reasoning Causal Reasoning in Language Models
The Key Laboratory of Cognition and Decision Intelligence for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China · School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
From Causal Discovery to Dynamic Causal Inference in Neural Time Series
Mar 21, 2026Dmitry Zaytsev, Valentina Kuskova, Michael CoppedgeCausal Inference Time-Series Causal Discovery
Lucy Family Institute for Data & Society, University of Notre Dame, Notre Dame, Indiana, USA · Political Science, University of Notre Dame, Notre Dame, Indiana, USA
Improving RCT-Based Treatment Effect Estimation Under Covariate Mismatch via Calibrated Alignment
Mar 19, 2026Amir Asiaee, Samhita PalCausal Effect Estimation Causal Representation Learning
Department of Biostatistics, Vanderbilt University Medical Center, TN 37203, USA
CausalVAD: De-confounding End-to-End Autonomous Driving via Causal Intervention
Mar 19, 2026Jiacheng Tang, Zhiyuan Zhou, Zhuolin He +3End-to-End Autonomous Driving Autonomous Driving Planning
Fudan University · Beijing Institute of Technology · East China Normal University +1
The Causal Uncertainty Principle: Manifold Tearing and the Topological Limits of Counterfactual Interventions
Mar 18, 2026Rui Wu, Hong Xie, Yongjun LiCausal Intervention Causal Inference
School of Management, University of Science and Technology of China 96 Jinzhai Road, Hefei, 230026, Anhui, China · School of Computer Science and Engineering, University of Science and Technology of China 96 Jinzhai Road, Hefei, 230026, Anhui, China
Cohomological Obstructions to Global Counterfactuals: A Sheaf-Theoretic Foundation for Generative Causal Models
Mar 18, 2026Rui Wu, Hong Xie, Yongjun LiCausal Counterfactual Generation Causal Inference
School of Management, University of Science and Technology of China · School of Computer Science and Engineering, University of Science and Technology of China
Conditional Distributional Treatment Effects: Doubly Robust Estimation and Testing
Mar 17, 2026Saksham Jain, Alex LuedtkeCausal Effect Estimation Causal Inference
Department of Statistics, University of Washington, Seattle, USA · Departments of Health Care Policy and Statistics, Harvard University, USA
InterveneBench: Benchmarking LLMs for Intervention Reasoning and Causal Study Design in Real Social Systems
Mar 16, 2026Shaojie Shi, Zhengyu Shi, Lingran Zheng +15Causal Reasoning in Language Models Causal Inference
Fudan University · Shanghai Innovation Institute
Point-Identification of a Robust Predictor Under Latent Shift with Imperfect Proxies
Mar 16, 2026Zahra Rahiminasab, Reza Soumi, Arto Klami +1Proximal Causal Inference Distribution Shift Robustness
Department of Computer Science, Aalto University · Department of Computer Science, University of Helsinki · ELLIS Institute Finland +1
Causal Cellular Context Transfer Learning (C3TL): An Efficient Architecture for Prediction of Unseen Perturbation Effects
Mar 13, 2026Michael Scholkemper, Sach MukherjeeTransfer Learning Cross-Domain Transfer Learning
Statistics and Machine Learning, German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany. · Statistics and Machine Learning, German Center for Neurodegenerative Diseases (DZNE) Bonn, Germany, University of Bonn, Germany & University of Cambridge, United Kingdom.
Multi-Task Anti-Causal Learning for Reconstructing Urban Events from Residents' Reports
Mar 12, 2026Liangkai Zhou, Susu Xu, Shuqi Zhong +1Structural Causal Models Multi-Task Learning
Stony Brook University Stony Brook, New York, United States · Johns Hopkins University Baltimore, Maryland, United States
LLM-Assisted Causal Structure Disambiguation and Factor Extraction for Legal Judgment Prediction
Mar 12, 2026Yuzhi Liang, Lixiang Ma, Xinrong ZhuLegal Judgment Prediction Legal Document Analysis
School of Information Technology, Guangdong University of Foreign Studies
RCTs for Frontier AI Governance: Methodological Challenges and Solutions for Human Uplift Studies
Mar 11, 2026Patricia Paskov, Kevin Wei, Shen Zhou Hong +7Causal Inference AI Governance
RAND, Santa Monica, CA 90407, United States · Johns Hopkins University, Baltimore, MD 21218, United States · Cornell University, Ithaca, NY 14853, United States +4

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Dual Causal Inference: Integrating Backdoor Adjustment and Instrumental Variable Learning for Medical VQA

Zibo Xu, Qiang Li, Ke Lu, Jin Wang+2

School of Microelectronics, Tianjin University, Tianjin 300072, China · Department of Orthopedics, Affiliated Kunshan Hospital of Jiangsu University, Jiangsu 215300, China · Department of Clinical Laboratory, The Third Central Hospital of Tianjin, Tianjin 300170, China+1

Multimodal Robustness Medical VQA Causal Inference

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Medical Visual Question Answering (MedVQA) aims to generate clinically reliable answers conditioned on complex medical images and questions. However, existing methods often overfit to superficial cross-modal correlations, neglecting the intrinsic biases embedded in multimodal medical data. Consequently, models become vulnerable to cross-modal confounding effects, severely hindering their ability to provide trustworthy diagnostic reasoning. To address this limitation, we propose a novel Dual Causal Inference (DCI) framework for MedVQA. To the best of our knowledge, DCI is the first unified architecture that integrates Backdoor Adjustment (BDA) and Instrumental Variable (IV) learning to jointly tackle both observable and unobserved confounders. Specifically, we formulate a Structural Causal Model (SCM) where observable cross-modal biases (e.g., frequent visual and textual co-occurrences) are mitigated via BDA, while unobserved confounders are compensated using an IV learned from a shared latent space. To guarantee the validity of the IV, we design mutual information constraints that maximize its dependence on the fused multimodal representations while minimizing its associations with the unobserved confounders and target answers. Through this dual mechanism, DCI extracts deconfounded representations that capture genuine causal relationships. Extensive experiments on four benchmark datasets, SLAKE, SLAKE-CP, VQA-RAD, and PathVQA, demonstrate that our method consistently outperforms existing approaches, particularly in out-of-distribution (OOD) generalization. Furthermore, qualitative analyses confirm that DCI significantly enhances the interpretability and robustness of cross-modal reasoning by explicitly disentangling true causal effects from spurious cross-modal shortcuts.