Causal Inference

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Overview

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

CardsList

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

  1. Copula-ResLogit: A Deep-Copula Framework for Unobserved Confounding Effects

    Mar 11, 2026Kimia Kamal, Bilal FarooqCausal Inference with Unobserved ConfoundingCausal Inference

    Laboratory of Innovations in Transportation (LiTrans), Toronto Metropolitan University, Toronto, Canada

  2. CIGPose: Causal Intervention Graph Neural Network for Whole-Body Pose Estimation

    Mar 10, 2026Bohao Li, Zhicheng Cao, Huixian Li +1Human Pose EstimationCausal Inference

    School of Computer Science, Northwestern Polytechnical University · Xidian University · School of Cybersecurity, Northwestern Polytechnical University

  3. Proxy-Guided Measurement Calibration

    Mar 10, 2026Saketh Vishnubhatla, Shu Wan, Andre Harrison +2Proximal Causal InferenceCausal Inference

    Arizona State University, Tempe, AZ · DEVCOM Army Research Lab, Adelphi, MD

  4. Statistical Inference via Generative Models: Flow Matching and Causal Inference

    Mar 9, 2026Shinto EguchiCausal InferenceFlow Matching

  5. How Wrong Can Your Counterfactual Be? Quantifying Confounding Bias for Continuous Treatments without a Control Group

    Mar 8, 2026Yu Wang, Xiangchen Liu, Siguang LiCausal Effect EstimationCausal Inference

    Department of Economics, Cornell University. · Department of Family and Consumer Sciences, California State University, Long Beach. · Society Hub, Hong Kong University of Science and Technology (Guangzhou).

  6. Masking Causality and Conditional Dependence

    Mar 7, 2026Zou Yang, Sophia Xiao, Bijan MazaheriCausal InferenceAlgorithmic Fairness

    Thayer School of Engineering Dartmouth College Hanover, NH, 03755

  7. Debiasing Sequential Recommendation with Time-aware Inverse Propensity Scoring

    Mar 5, 2026Sirui Huang, Jing Long, Qian Li +2Exposure BiasSequential Recommendation

    Hong Kong Polytechinic University Hong Kong SAR, China · University of Technology Sydney Sydney, Australia · Curtin University Perth, Australia +1

  8. Observationally Informed Adaptive Causal Experimental Design

    Mar 4, 2026Erdun Gao, Liang Zhang, Jake Fawkes +5Active Experimental DesignCausal Inference

    Australian Institute for Machine Learning, Adelaide University · Hong Kong University of Science and Technology (Guangzhou) · University College London +3

  9. Controllable Generative Sandbox for Causal Inference

    Mar 3, 2026Qi Zhang, Harsh Parikh, Ashley Naimi +3Synthetic Data GenerationCausal Inference

    Emory University · Yale University · Amgen

  10. Generalized Bayes for Causal Inference

    Mar 3, 2026Emil Javurek, Dennis Frauen, Yuxin Wang +1Generalized Bayesian InferenceCausal Inference

    LMU Munich · Munich Center for Machine Learning

  11. Distributed Dynamic Invariant Causal Prediction in Environmental Time Series

    Mar 3, 2026Ziruo Hao, Tao Yang, Xiaofeng Wu +1Causal InferenceTime-Series Causal Discovery

  12. Estimating Visual Attribute Effects in Advertising from Observational Data: A Deepfake-Informed Double Machine Learning Approach

    Mar 2, 2026Yizhi Liu, Balaji Padmanabhan, Siva ViswanathanCausal Effect EstimationCausal Inference

    Robert H. Smith School of Business, University of Maryland, College Park, MD, USA

  13. De-paradox Tree: Breaking Down Simpson's Paradox via A Kernel-Based Partition Algorithm

    Mar 2, 2026Xian Teng, Yu-Ru LinCausal InferenceCausal Reasoning

    University of Pittsburgh, Pittsburgh, Pennsylvania, USA

  14. Instrumental and Proximal Causal Inference with Gaussian Processes

    Mar 2, 2026Yuqi Zhang, Krikamol Muandet, Dino Sejdinovic +2Proximal Causal InferenceCausal Inference

    Department of Statistics and Actuarial Science, University of Hong Kong, Hong Kong · Rational Intelligence Lab, CISPA Helmholtz Center for Information Security, Germany · Australian Institute for Machine Learning, Adelaide University, Australia +1

  15. Causal Neural Probabilistic Circuits

    Mar 2, 2026Weixin Chen, Han ZhaoConcept Bottleneck ModelsCausal Intervention

    University of Illinois Urbana-Champaign

  16. Causal Effects with Unobserved Unit Types in Interacting Human-AI Systems

    Mar 2, 2026William Overman, Sadegh Shirani, Mohsen BayatiCausal Effect EstimationCausal Inference

    Graduate School of Business, Stanford University

  17. Fairness under Graph Uncertainty: Achieving Interventional Fairness with Partially Known Causal Graphs over Clusters of Variables

    Feb 27, 2026Yoichi ChikaharaAlgorithmic FairnessCausal Inference

    NTT Communication Science Laboratories, Kyoto, Japan

  18. Multi-Agent Causal Reasoning for Suicide Ideation Detection Through Online Conversations

    Feb 27, 2026Jun Li, Xiangmeng Wang, Haoyang Li +6Suicide Risk ModelingSocial Media Analysis

    The Hong Kong Polytechnic University, Hong Kong, China · City University of Hong Kong, Hong Kong, China · Shenzhen MSU-BIT University, Shenzhen, China +1

  19. Causal Identification from Counterfactual Data: Completeness and Bounding Results

    Feb 26, 2026Arvind Raghavan, Elias BareinboimCausal InferenceCausal Identifiability

    Causal Artificial Intelligence Lab, Department of Computer Science, Columbia University

  20. Sequential Counterfactual Inference for Temporal Clinical Data: Addressing the Time Traveler Dilemma

    Feb 24, 2026Jingya Cheng, Alaleh Azhir, Jiazi Tian +1Counterfactual ExplanationsCounterfactual Reasoning

    Massachusetts General Hospital, Boston, MA, USA

  21. CausalReasoningBenchmark: A Real-World Benchmark for Disentangled Evaluation of Causal Identification and Estimation

    Feb 24, 2026Ayush Sawarni, Jiyuan Tan, Vasilis SyrgkanisCausal Effect EstimationBenchmark Design

    Stanford University

  22. cc-Shapley: Measuring Multivariate Feature Importance Needs Causal Context

    Feb 23, 2026Jörg Martin, Stefan HaufeFeature AttributionShapley Value Attribution

    Physikalisch-Technische Bundesanstalt, Berlin, Germany · Technische Universität Berlin, Berlin, Germany · Charité – Universitätsmedizin Berlin, Berlin, Germany

  23. CausalFlip: A Benchmark for LLM Causal Judgment Beyond Semantic Matching

    Feb 23, 2026Yuzhe Wang, Yaochen Zhu, Jundong LiLLM EvaluationCausal Reasoning in Language Models

    University of Virginia Charlottesville, Virginia, USA

  24. DoAtlas-1: A Causal Compilation Paradigm for Clinical AI

    Feb 22, 2026Yulong Li, Jianxu Chen, Xiwei Liu +9Causal ReasoningCausal Inference

    Mohamed bin Zayed University of Artificial Intelligence · Xi’an Jiaotong-Liverpool University.

  25. Test-Time Learning of Causal Structure from Interventional Data

    Feb 22, 2026Wei Chen, Rui Ding, Bojun Huang +5Causal Structure LearningCausal Inference

    HKUST · HKUST(GZ) · Microsoft Research +2

  26. Optimizing Graph Causal Classification Models: Estimating Causal Effects and Addressing Confounders

    Feb 20, 2026Simi Job, Xiaohui Tao, Taotao Cai +3Graph Neural NetworksCausal Inference

    School of Mathematics, Physics, and Computing, University of Southern Queensland, Australia · School of Data Science, Lingnan University, Hong Kong · School of Business, University of Southern Queensland, Australia +1

  27. Causal Neighbourhood Learning for Invariant Graph Representations

    Feb 20, 2026Simi Job, Xiaohui Tao, Taotao Cai +2Graph Representation LearningCausal Representation Learning

    School of Science, Engineering and Digital Technologies, University of Southern Queensland, Australia. · School of Data Science, Lingnan University, Hong Kong.

  28. General sample size analysis for probabilities of causation: a delta method approach

    Feb 19, 2026Tianyuan Cheng, Ruirui Mao, Judea Pearl +1Causal Inference

    Department of Statistics, Florida State University · Department of Computer Science, Cornell University · Department of Computer Science, University of California Los Angeles +1

  29. Causality is Key for Interpretability Claims to Generalise

    Feb 18, 2026Shruti Joshi, Aaron Mueller, David Klindt +3LLM InterpretabilityCausal Interpretability

    Mila - Québec AI Institute & Université de Montréal · Boston University · Cold Spring Harbor Laboratory +1

  30. This human study did not involve human subjects: Validating LLM simulations as behavioral evidence

    Feb 17, 2026Jessica Hullman, David Broska, Huaman Sun +1Large Language Model-Based Social SimulationHuman Behavior Simulation

    Northwestern University · Stanford University