Structural Causal Models

Also known as SCM

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Overview

Structural causal models represent how variables are generated by mechanisms, often organized in a graph, so they can answer questions about interventions and counterfactuals rather than correlations alone. Researchers learn these models from observational and experimental data using constraint- and score-based graph discovery, linear and non-Gaussian structural equation models, nonlinear neural or symbolic mechanisms, and invariance across environments; latent confounding, feedback, and time-varying systems extend the basic acyclic setting. The models support treatment-effect estimation, scientific mechanism analysis, fairness and recourse, anomaly diagnosis, and causal world models, with evaluation increasingly using synthetic benchmarks and intervention-based tests. A central challenge is that observational data often leave multiple structures or counterfactuals compatible with the evidence, so progress depends on explicit assumptions, informative interventions, and reliable uncertainty and benchmark evaluation.

Latest papers

CardsList

144 papers16 papers in the last four weeks, up 7% on the four weeks before. 0.1% of all new papers.

  1. DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis

    Apr 5, 2026Hristo Petkov, Calum MacLellan, Feng DongCausal Structure LearningTabular Data Synthesis

    Department of Computer and Information Sciences, University of Strathclyde, 16 Richmond Street, Glasgow, G1 1XQ, Lanarkshire, United Kingdom.

  2. Procela: Epistemic Governance in Mechanistic Simulations Under Structural Uncertainty

    Apr 1, 2026Kinson VernetEpistemic UncertaintyUncertainty Quantification

    Independent Researcher

  3. On the Relationship between Bayesian Networks and Probabilistic Structural Causal Models

    Mar 28, 2026Peter J. F. Lucas, Eleonora Zullo, Fabio StellaStructural Causal ModelsBayesian Networks

    ICIS, Radboud University, Nijmegen, the Netherlands · Dipartimento di Informatica, Sistemistica e comunicazion University of Milano-Bicocca, Milan, Italy

  4. Kolmogorov-Arnold causal generative models

    Mar 20, 2026Alejandro Almodóvar, Mar Elizo, Patricia A. Apellániz +2Generative ModelingStructural Causal Models

  5. Teleological Inference in Structural Causal Models via Intentional Interventions

    Mar 19, 2026Dario Compagno, Fabio Massimo ZennaroStructural Causal ModelsCausal Intervention

  6. Cohomological Obstructions to Global Counterfactuals: A Sheaf-Theoretic Foundation for Generative Causal Models

    Mar 18, 2026Rui Wu, Hong Xie, Yongjun LiCausal Counterfactual GenerationCausal Inference

    School of Management, University of Science and Technology of China · School of Computer Science and Engineering, University of Science and Technology of China

  7. Multi-Task Anti-Causal Learning for Reconstructing Urban Events from Residents' Reports

    Mar 12, 2026Liangkai Zhou, Susu Xu, Shuqi Zhong +1Structural Causal ModelsMulti-Task Learning

    Stony Brook University Stony Brook, New York, United States · Johns Hopkins University Baltimore, Maryland, United States

  8. Interventional Time Series Priors for Causal Foundation Models

    Mar 11, 2026Dennis Thumm, Ying ChenCausal Foundation ModelsSynthetic Data Generation

    National University of Singapore · Department of Mathematics, Centre for Quantitative Finance, Risk Management Institute, National University of Singapore

  9. Structural Causal Bottleneck Models

    Mar 9, 2026Simon Bing, Jonas Wahl, Jakob RungeStructural Causal ModelsCausal Effect Estimation

    Equal contribution · Technische Universität Berlin · Department of Computer Science, University of Potsdam +1

  10. Sign Identifiability of Causal Effects in Stationary Stochastic Dynamical Systems

    Mar 9, 2026Gijs van Seeventer, Saber SalehkaleybarStochastic Differential EquationsStructural Causal Models

    1LIACS, Leiden University, Netherlands

  11. Distributional Equivalence in Linear Non-Gaussian Latent-Variable Cyclic Causal Models: Characterization and Learning

    Mar 5, 2026Haoyue Dai, Immanuel Albrecht, Peter Spirtes +1Causal DiscoveryCausal Discovery with Latent Confounding

    Carnegie Mellon University · FernUniversität in Hagen · MBZUAI

  12. Scalable Contrastive Causal Discovery under Unknown Soft Interventions

    Mar 3, 2026Mingxuan Zhang, Khushi Desai, Sopho Kevlishvili +1Causal DiscoveryStructural Causal Models

    Department of Systems Biology, Columbia University · Irving Institute for Cancer Dynamics, Columbia University · Department of Computer Science, Columbia University +2

  13. SphUnc: Hyperspherical Uncertainty Decomposition and Causal Identification via Information Geometry

    Mar 1, 2026Rong Fu, Chunlei Meng, Jinshuo Liu +8Uncertainty DecompositionUncertainty Calibration

    Independent Researcher

  14. Causal Mechanism Reduction: Mechanism Replacement for Neural Network Pruning and Abstraction

    Feb 27, 2026Amir AsiaeeStructural Causal ModelsCausal Abstraction

    Vanderbilt University Medical Center Nashville, TN

  15. Operationalizing Longitudinal Causal Discovery Under Real-World Workflow Constraints

    Feb 27, 2026Tadahisa Okuda, Shohei Shimizu, Thong Pham +2Causal DiscoveryCausal Structure Learning

    Kyoto University Graduate School of Medicine, Kyoto, Japan · SANKEN, The University of Osaka, Osaka, Japan · Faculty of Data Science, Shiga University, Shiga, Japan +3

  16. Inferential Mechanics Part 1: Causal Mechanistic Theories of Machine Learning in Chemical Biology with Implications

    Feb 26, 2026Ilya Balabin, Thomas M. KaiserCausal InterpretabilityStructural Causal Models

    Avicenna Biosciences, Inc., Durham, NC, USA

  17. Multi-Level Causal Embeddings

    Feb 25, 2026Willem Schooltink, Fabio Massimo ZennaroStructural Causal ModelsCausal Abstraction

    Department of Informatics, University of Bergen

  18. Causal and Compositional Abstraction

    Feb 18, 2026Robin Lorenz, Sean TullCategory TheoryCausal Abstraction

    Quantinuum, London, United Kingdom

  19. Root Cause Analysis of Outliers in Unknown Cyclic Graphs

    Oct 8, 2025Daniela Schkoda, Dominik JanzingStructural Causal ModelsRoot Cause Analysis

    Amazon Research Tübingen, Germany · Technical University of Munich, Germany

  20. Causally Fair Node Classification on Non-IID Graph Data

    May 3, 2025Yucong Dai, Lu Zhang, Yaowei Hu +2Structural Causal ModelsGraph Representation Learning

    Clemson University · University of Arkansas · Walmart Inc.

  21. Multi-View Causal Discovery without Non-Gaussianity: Identifiability and Algorithms

    Feb 27, 2025Ambroise Heurtebise, Omar Chehab, Pierre Ablin +2Structural Causal ModelsCausal Discovery

    Inria, CEA, University of Paris-Saclay, France · Carnegie Mellon University, Pittsburgh PA, USA · ENSAE, CREST, IP Paris, France +2

  22. What is causal about causal models and representations?

    Jan 31, 2025Frederik Hytting Jørgensen, Luigi Gresele, Sebastian WeichwaldStructural Causal ModelsCausal Intervention

    Copenhagen Causality Lab, Department of Mathematical Sciences, and Pioneer Centre for AI, University of Copenhagen, Denmark

  23. Towards Complete Causal Explanation with Expert Knowledge

    Jul 10, 2024Aparajithan Venkateswaran, Emilija PerkovićStructural Causal ModelsCausal Discovery

    Microsoft · Redmond, WA 98052, USA · Department of Statistics +2

  24. Invariant Graph Representations for Continuous-Time Dynamic Graphs Under Distribution Shifts

    May 29, 2024Lanting Fang, Yulian Yang, Yawei Zhang +3Causal Representation LearningStructural Causal Models

    1Beijing Institue of Technology · Southeast University · Wuhan University