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.

Latest papers

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

Doubly Robust Deep Kernel Tests for Structured Outcomes
Oct 27, 2026Robin Leman, Bariscan Bozkurt, Sofiane Tanji +2Two-Sample Testing Causal Inference
University College London · Gatsby Computational Neuroscience Unit, University College London · ICTEAM/INMA, UCLouvain
A Hybrid Framework for Explainable Aviation Risk: Causal Chain Extraction from Safety Reports with Physics-Based Failure Quantification and Interventional Analysis
Oct 26, 2026Yutian Pang, Yifang Tian, Jiacheng Xie +4Aviation Safety Causal Inference
Department of Aerospace Engineering and Engineering Mechanics, The University of Texas at Austin, Austin, TX 78712, USA. · Department of Electrical and Computer Engineering, University of Toronto, Toronto, ON M5S 3G4, Canada. · Daniel Guggenheim School of Aerospace Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA. +2
When Does Pre-Trained Capacity Help Causal Learning? From Scalar Prediction to Counterfactual Post-Training
Oct 23, 2026Qi Xu, Zijie Lei, Zhifei Deng +2Causal Inference Causal Effect Estimation
Meta Platforms, Inc., Sunnyvale, CA, USA
Multi-Agent Retrieval-Augmented Causality Assessment (MARCA) of Individual Case Safety Reports in Pharmacovigilance
Oct 23, 2026Daisy Rae Lopez, Angela Boccia, Maurizio SessaClinical Decision Support Causal Inference
Department of Drug Design and Pharmacology, University of Copenhagen, Copenhagen, Denmark · Department of Medical and Surgical Sciences (DIMEC), University of Bologna, Bologna, Italy
PriMa-Causa: Causal Prescriptive Maintenance
Oct 22, 2026Felix SaretzkyCausal Inference Causal Effect Estimation
Towards Realistic Models of Change: A Causal Perspective on Decision-Making and Dynamical Systems
Oct 20, 2026Isacco BerettaCausal Inference Algorithmic Recourse
Università di Pisa National Ph.D. in Artificial Intelligence XXXVII cycle
Unified Sharp Bounds on Probabilities of Causation
Oct 19, 2026Xin Shu, Ang LiCausal Inference Partial Identification
Department of Computer Science, Florida State University
Estimating Causal Bounds from Observational Data With Neural Generative Models
Oct 18, 2026Max Zhu, Srivathsan Amruth, Sina Akbari +1Causal Inference Causal Effect Estimation
Department of Pure Mathematics and Mathematical Statistics University of Cambridge
CaMeWM: A Causal Medical World Model for Generative Tumor Evolution and Treatment Planning
Oct 17, 2026Huanlong Gao, Xuelei He, Fengjun Zhao +1Causal World Models Causal Counterfactual Generation
School of Electronic Information, Northwest University, No. 1 Xuefu Avenue, Xi’an, 710127, China · Xi’an Key Lab of Radiomics and Intelligent Perception, Northwest University, No. 1 Xuefu Avenue, Xi’an, 710127, China
Concordance Plausibility Check for Intervention-Conditioned Human Digital Twins
Oct 17, 2026Saeed Farzi, Christopher Kipps, John McBeth +7Counterfactual Evaluation Digital Twins
School of Electronics and Computer Science, University of Southampton, Southampton, UK · Faculty of Medicine, University of Southampton, Southampton, UK · University Hospital Southampton NHS Foundation Trust, Southampton, UK +9
Graph-Regularized Synthetic Control for Stable Counterfactual Estimation under Donor Redundancy
Oct 16, 2026Yanlin Zhang, Sungyong Chung, Alireza TalebpourSynthetic Control Causal Effect Estimation
University of Illinois Urbana-Champaign
DeDiCa: A Scalable Deconfounding Discrete Causal Generative Model
Oct 16, 2026Kunchangtai Liang, Adrián Javaloy, Antonio Vergari +1Causal Inference Causal Effect Estimation
Department of Computer Science, Saarland University, Saarbrücken, Germany · Department of Computer Science, University of Bath, Bath, United Kingdom · School of Informatics, University of Edinburgh, Edinburgh, United Kingdom
Causal Value Attribution of Marketing Labels in LLM-Generated Ads: A Deconfounder Approach
Oct 16, 2026Danhong You, Wei Jiang, Zhiqiang +1Causal Inference Causal Inference with Unobserved Confounding
Eric · Shanghai Jiao Tong University · University of Texas at Dallas
Flexible Doubly Robust Proximal Causal Inference for Censored Outcomes
Oct 14, 2026Dhanajit Brahma, Ruolin Meng, Lawrence Carin +1Proximal Causal Inference Causal Effect Estimation
Duke University
Learning Who Benefits Most: Multi-Treatment Causal Ranking for Population Health Stratification
Oct 14, 2026Rocco Zaccagnino, Martina Girolamo, Alberto Lombardi +3Clinical Decision-Making Causal Inference
University of Salerno, Salerno, Italy · Local Health Authority of Benevento, Benevento, Italy · University of California, Los Angeles, Los Angeles, USA
ACED-Bench: Evaluating How LLM Agents Acquire and Act on Causal Evidence
Oct 9, 2026Yiwei Chen, Amila Weerasinghe, Siddharth Biswal +4AI Agent Benchmarks Causal Reasoning in Language Models
UC San Diego · Amazon
Amortized Counterfactual Prediction with Graph Neural Networks
Oct 9, 2026Hendrik Stumpf, Francesco QuinzanCounterfactual Prediction Causal Inference
Department of Computer Science, University of Oxford · Department of Engineering, University of Oxford
One Thing Leads to Another: The Essentials of Time Series Causal Discovery
Oct 9, 2026Kurt Butler, Damian Machlanski, Michael P. J. Camilleri +5Time-Series Causal Discovery Causal Discovery
School of Engineering, The University of Edinburgh, and the Causality in Healthcare AI Hub (CHAI) · School of Informatics, The University of Edinburgh and CHAI · Department of Statistical Science, University College London, and CHAI
Causal-fate dynamics of unrealized influence
Oct 8, 2026Yiwei Liu, Luwei Yang, Shunbo LeiLatent Dynamics Modeling Causal Inference
School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, Guangdong, 518172, China. · Shenzhen Research Institute of Big Data (SRIBD), Shenzhen, 518172, China.
OrthoGen: A Generative Orthogonal Learner for Time-Varying Treatments
Oct 7, 2026Tomàs Garriga, Valentyn Melnychuk, Konstantin Hess +4Causal Effect Estimation Causal Inference
Novartis · Barcelona Supercomputing Center · Universitat Polit`ecnica de Catalunya +2
Symmetry-Informed Causal Partial Identification
Oct 6, 2026Uzair Akbar, Zulfiqar Zaidi, Niki Kilbertus +2Causal Inference Causal Effect Estimation
Georgia Tech · TU Munich · Helmholtz Munich +2
Probabilistic Counterfactual Inference for Discrete Outcomes in Gaussian-Process Causal Models
Oct 6, 2026Juliette Sinnott, Amir-Hossein Karimi, Mohammad KohandelStructural Causal Models Causal Counterfactual Generation
Department of Applied Mathematics, University of Waterloo, Waterloo, Ontario, N2L 3G1, Canada · Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, Ontario, N2L 3G1, Canada · Vector Institute for Artificial Intelligence, Toronto, Ontario, M5G 0C6, Canada
ProximalFM: Amortized Proximal Causal Inference under Hidden Confounding
Oct 6, 2026Christophe Muller, Ayub Kharel, Alex Luedtke +6Causal Inference Amortized Inference
University of Oxford · Harvard University · University of Illinois Urbana-Champaign +5
sHAIL-Causal: A Sequential Staircase Procedure for Invariant Causal Predictor Discovery
Oct 5, 2026Ernest FokouéCausal Discovery Causal Inference
School of Mathematics and Statistics, Rochester Institute of Technology
Proximal Causal Learning under Unmeasured Confounding
Oct 3, 2026Ying Tang, Yi WangProximal Causal Inference Causal Effect Estimation
Shanghai University of International Business and Economics, Shanghai 201620, China
Causally Fair Generation with Large Language Models
Oct 3, 2026Patrik Okanovic, Torsten Hoefler, Drago PleckoAlgorithmic Fairness Causal Interventions in Language Models
Department of Computer Science, ETH Zurich · Department of Statistics & Data Science, UCLA
Peer Effects in Signed Networks: Separating Influence Through Positive and Negative Ties
Oct 2, 2026Xiaojing Du, Jiuyong Li, Lin Liu +3Causal Effect Estimation Causal Inference
Adelaide University, Adelaide, Australia · Hainan University, Haikou, China
ROUTEAUDIT: Interaction-Aware Identification for Budgeted Multi-Verifier Routing
Oct 2, 2026Miaobo Hu, Shuhao Hu, Xiaobo Guo +4Adaptive Model Routing Algorithmic Auditing
School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China · Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China
Causal Memory Policy: Making Memory Utility Identifiable by Intervening on Retrieval
Oct 1, 2026Arman Behnam, Binghui WangLLM Memory Causal Inference
Department of Computer Science Illinois Institute of Technology Chicago, IL
Counterfactual Predictions in Scientific Emulators Without Controlled Experiments
Sep 30, 2026Dingling Yao, Kahaan Gandhi, Valentin Duruisseaux +3Counterfactual Prediction Causal Inference
Institute of Science and Technology Austria · California Institute of Technology · NVIDIA

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Doubly Robust Deep Kernel Tests for Structured Outcomes

Robin Leman, Bariscan Bozkurt, Sofiane Tanji, Arthur Gretton+1

University College London · Gatsby Computational Neuroscience Unit, University College London · ICTEAM/INMA, UCLouvain

Two-Sample Testing Causal Inference Deep Kernel Learning Doubly Robust Estimation

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We propose a test of whether two interventions induce the same outcome distribution when outcomes are structured and high-dimensional, such as images, text and graphs. We represent the interventional distributions as mean embeddings on learned deep features, such that the treatment effect is a doubly robust representation of the difference in mean embeddings. Our test, DR-KTE-Deep, optimises the learned features so as to maximise an estimated power criterion on this treatment effect representation. To motivate our learning objective, we show that the power criterion characterises the asymptotic signal-to-noise ratio of the test under fixed alternatives, and we derive a uniform finite-sample bound for its regularised estimate. We show that, with the learned kernel fixed, a held-out test is asymptotically valid under the null given suitable nuisance-estimation rates. Empirically, DR-KTE-Deep achieves substantially higher power than fixed and bandwidth-optimised kernel baselines across image, text and graph outcomes, while maintaining well-calibrated Type-I error.