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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CausalPOI: Spatio-Temporal Graph-Based Causal Modeling for Cold-Start POI Check-in Forecasting
Jun 3, 2026Zhaoqi Zhang, Miao Xie, Yi Li +3Causal Representation Learning Causal Inference
Nanyang Technological University Singapore, Singapore · China Agricultural University Beijing, China · Meituan Beijing, China
Reconciling Causality and Non-Equilibrium Thermodynamics with Hamiltonian Causal Models
Jun 3, 2026Dario Rancati, Max Welling, Francesco LocatelloDynamical Systems Causal Inference
Institute of Science and Technology Austria · University of Amsterdam
Using Text-Based Causal Inference to Disentangle Factors Influencing Online Review Ratings
Jun 2, 2026Linsen Li, Aron Culotta, Nicholas MatteiCausal Effect Estimation Causal Inference
Department of Computer Science Tulane University, New Orleans, LA, USA
Adaptive Causal Alignment for High-Confidence Adversarial Training
Jun 2, 2026Zhiming Luo, Kejia Zhang, Yingxin Lai +3Adversarial Training Adversarial Robustness
Department of Artificial Intelligence, Xiamen University, Xiamen, China · Department of Computer Science, Emory University, GA, USA · College of Information Science and Technology, Jinan University, Guangzhou, China
Privacy-Robust Incrementality Measurement for Advertising Systems under Signal Loss
Jun 2, 2026Prashant Shekhar, Caroline HowardOnline Advertising Partial Identification
Department of Mathematics Embry-Riddle Aeronautical University Daytona Beach, FL, USA
Tailoring Strictly Proper Scoring Rules for Downstream Tasks: An Application to Causal Inference
Jun 2, 2026Roman Plaud, Alexandre Perez-Lebel, Antoine Saillenfest +4Inverse Probability Weighting Causal Effect Estimation
Institut Polytechnique de Paris · Onepoint, France · Fundamental Technologies +2
Do Real-World Datasets Contain Natural Experiments? An Empirical Study Using Causal Feature Selection
Jun 2, 2026Gautam Gare, John Galeotti, Michael Mozer +2Causal Inference Causal Intervention
Carnegie Mellon University · Google DeepMind
Extending Causal Metamodeling to a non-Markovian Queue
May 30, 2026Pracheta Amaranath, Anant Bhide, David Jensen +1Surrogate Modeling Bayesian Networks
Manning College of Information and Computer Sciences, University of Massachusetts Amherst, MA, USA
Perturbative methods for non-parametric instrumental variable
May 29, 2026Wei Bu, Arthur GrettonKernel Ridge Regression Instrumental Variable Estimation
Harvard University, Northeastern University, Physics Department · University College London.
Evaluating Bivariate Causal Statements Based on Mutual Compatibility
May 29, 2026Erik Jahn, Dominik JanzingStructural Causal Models Causal Reasoning
1California Institute of Technology, USA · 2Amazon Research Tübingen, Germany
Formalizing and falsifying causal pathways of rare events
May 29, 2026Anahita Haghighat, Dominik JanzingStructural Causal Models Causal Abstraction
Independent · Amazon Research, Tübingen, Germany
YoCausal: How Far is Video Generation from World Model? A Causality Perspective
May 28, 2026You-Zhe Xie, Yu-Hsuan Li, Jie-Ying Lee +3Video Diffusion Models Causal World Models
National Yang Ming Chiao Tung University · Shanda AI Research Tokyo
Test Time Training for Supervised Causal Learning
May 28, 2026Zizhen Deng, Jiaru Zhang, Rui Ding +5OOD Generalization Causal Discovery
Peking University · The work was done during his internship at Microsoft. · Shanghai Jiao Tong University +2
Predicting Causal Effects from Natural Language Queries using Structured Representations
May 28, 2026Giuliano Martinelli, Piriyakorn Piriyatamwong, Abelardo Carlos Martinez Lorenzo +7Causal Effect Estimation Causal Inference
1The World Bank Group · University of Oxford · 3New York University
Causal Risk Minimization for High-Dimensional Treatments
May 26, 2026Nikita Dhawan, Arnav Paruthi, Andrew Kim +3Causal Effect Estimation Causal Inference
University of Toronto, Vector Institute · University of Toronto · Vanguard
Towards Continuous-time Causal Foundation Models
May 26, 2026Dennis Thumm, Ruben Wiedemann, Ying ChenIrregular Time-Series Modeling Causal Foundation Models
National University of Singapore · Department of Mathematics, Centre for Quantitative Finance, Risk Management Institute, National University of Singapore. · Imperial College London
Stop Suppressing the Tail: Causal Inference for Extreme Events
May 26, 2026Eichi UeharaCausal Effect Estimation Causal Inference
Calibrated Inference for the Conditional Average Treatment Effect in the Few-Placebo Regime via Gaussian Processes
May 26, 2026Eichi UeharaCausal Effect Estimation Conditional Average Treatment Effect Estimation
AFLO
Confounder Detection via Treatment Intent: A New Observational Study Design
May 26, 2026Drago Plecko, Patrik Okanovic, Torsten Hoefler +1Causal Inference
Department of Statistics & Data Science UCLA · Department of Computer Science ETH Zurich · Causal AI Lab Columbia University
Beyond Differences: Doubly Robust Meta-Learners for Ratio-Based Treatment Effects
May 25, 2026Michael Fuchs, Dominik KreissCausal Effect Estimation Conditional Average Treatment Effect Estimation
Actuarial Department Allianz Versicherungs-AG Munich, Germany
Causal methods for LLM development and evaluation
May 25, 2026Dennis Frauen, Marie Brockschmidt, Konstantin Hess +10LLM Evaluation Causal Inference
MCML & LMU Munich, Munich, Germany · CMU, Pittsburgh, US · Imperial College London, London, UK +1
Geometry Adaptive Counterfactual Distribution Learning with Diffusion-Guided Smoothing
May 25, 2026Kwangho KimSemiparametric Inference Causal Inference
Department of Statistics, Korea University
Learning Treatment Effects during Resource Allocation via Priority-Queue Randomization
May 24, 2026JungHo Lee, Johnna Sundberg, Pim Welle +1Causal Effect Estimation Instrumental Variable Estimation
1Carnegie Mellon University · 2Allegheny County Department of Human Services
Abduction-Deduction Entanglement: Domain Generalization via Representation Transplants
May 24, 2026Kasra Jalaldoust, Elias BareinboumDomain Generalization OOD Generalization
Department of Computer Science Columbia University New York, NY 10027, USA
Towards a Universal Causal Reasoner
May 24, 2026Qirun Dai, Xiao Liu, Jiawei Zhang +3Causal Reasoning in Language Models Causal Reasoning
1The University of Chicago · University of Illinois Urbana-Champaign
Measuring Alignment-Induced Activation Shifts Correctly: A Template-Controlled Difference-in-Differences Protocol
May 23, 2026Yuki NakamuraLLM Alignment Mechanistic Interpretability
The Open University of Japan
Hypothesis Generation and Inductive Inference in Children and Language Models
May 23, 2026Jeffrey Qin, Wasu Top Piriyakulkij, Zhuangfei Gao +4Bayesian Inference Scientific Hypothesis Generation
Computer Science University of Waterloo · Department of Computer Science Cornell University · Department of Computer Science Dalhousie University +1
Treatment Effect Estimation with Differentiated Networked Effect on Graph Data
May 23, 2026Xiaofeng Lin, Han Bao, Hisashi KashimaCausal Effect Estimation Causal Inference
Kyoto University Kyoto, Japan · The Institute of Statistical Mathematics Tokyo, Japan · Tohoku University Sendai, Japan +1
Leveraging Foundation Models for Causal Generative Modeling
May 22, 2026Aneesh Komanduri, Xintao WuCausal Counterfactual Generation Image Generation
University of Arkansas Fayetteville, Arkansas, USA
Causality as the Statistical Conscience of Artificial Intelligence: From Pearl's Ladder to Trustworthy Machines
May 22, 2026Ernest FokouéCausal Inference Responsible AI
School of Mathematics and Statistics, College of Science Rochester Institute of TechnologyMay Rochester, New York 14623, USA

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CausalPOI: Spatio-Temporal Graph-Based Causal Modeling for Cold-Start POI Check-in Forecasting

Zhaoqi Zhang, Miao Xie, Yi Li, Linyou Cai+2

Nanyang Technological University Singapore, Singapore · China Agricultural University Beijing, China · Meituan Beijing, China

Causal Representation Learning Causal Inference Spatiotemporal Forecasting Spatiotemporal GNNs

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As urban environments continue to evolve rapidly, accurately modeling the dynamic behaviour of Points of Interest is essential for supporting data-driven urban planning and commercial decision-making. While recent advancements in spatio-temporal graph learning have improved POI forecasting, most methods rely on proximity-based graphs and correlation-driven modeling, which overlook the functional dependencies between POIs and fail to capture the causal effects of urban interventions. In this paper, we introduce a novel research problem -- cold-start POI check-in forecasting, which aims to predict the future check-in pattern of a newly introduced POI, by modeling its temporal evolution and functional interactions with nearby POIs in a structured urban spatial context. To address these challenges, we propose CausalPOI, a spatio-temporal graph-based causal representation learning framework. CausalPOI leverages Spatio-Temporal Functional Interaction Graph to model semantic and spatial relationships between POIs, and constructs structurally aligned treatment and control graphs to simulate factual and counterfactual scenarios. Extensive experiments on real-world SafeGraph datasets demonstrate that CausalPOI significantly outperforms state-of-the-art baselines across the board, validating its effectiveness in spatio-temporal forecasting, semantic interaction modeling, and causal effect estimation, providing a more interpretable and actionable foundation for urban intervention analysis. Source code is available at Github.

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