Structural Causal Models

Also known as SCM

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

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

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
Causal Discovery for Linear Models with Latent Confounding via Multi-domain Identifiable Representation Learning
Oct 20, 2026Danru Xu, Aapo Hyvärinen, Sara MagliacaneCausal Discovery Causal Discovery with Latent Confounding
University of Amsterdam · Saarland University · University of Helsinki
A Semi-Analytical Inference Framework for Structural Causal Models
Oct 20, 2026Tatiana Boura, Robert PeharzStructural Causal Models
Graz University of Technology
What Moved the Alarm Score? Causal Attribution with Exact Computation and Scarce-Incident Estimation
Oct 20, 2026Masaaki Takada, Ryusei ShingakiCausal Attribution Shapley Value Attribution
Toshiba Corporation
lepidoptera: A Domain-Agnostic Engine for Causal Simulation with Mixed Variable Types, Vine Copula Dependence, and Individual-Level Counterfactual Propagation
Oct 16, 2026Danilo Augusto SartiCausal Counterfactual Generation Structural Causal Models
Lecturer in AI and Machine Learning, Royal College of Surgeons in Ireland, Dublin, Ireland
Learning Non-Gaussian SEMs with Conditional Risk, and Why the Direction of Recursion Matters
Oct 9, 2026Ujas Shah, Panagiota Birmpa, Rebecca MorrisonCausal Structure Learning Structural Causal Models
University of Colorado Boulder · Heriot-Watt University
DAG Structure Learning with Node-Varying Heavy-Tailed Disturbances
Oct 9, 2026Robin Francis, Sundeep Prabhakar ChepuriCausal Structure Learning Causal Discovery
Indian Institute of Science, Bangalore, India
AdaPS-LiNGAM: Adaptive Predecessor Selection for Linear Non-Gaussian Acyclic Models under Small-Sample Settings
Oct 7, 2026Shun Yanashima, Kentaro Kanamori, Hirofumi SuzukiCausal Discovery Causal Structure Learning
Artiﬁcial Intelligence Laboratory, Fujitsu Limited, 4-1-1 Kamikodanaka, Nakahara-ku, Kawasaki, 211-8588, Kanagawa, Japan.
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
Same Predictions, Different Harms: Causal Auditing of Patient World Models
Oct 4, 2026Yicheng Qi, Xiyi XiongStructural Causal Models Causal World Models
Imperial College London, London, United Kingdom
Reasoning Models Are Accurate but Unsound on Identification
Oct 2, 2026Arman Behnam, Binghui WangQuery Identifiability Causal Identifiability
Department of Computer Science Illinois Institute of Technology Chicago, IL, USA
Invariance of Clustering Operations in Causal Effect Identification
Oct 2, 2026Jani Nykänen, Otto Tabell, Santtu Tikka +1Causal Identifiability Causal Effect Estimation
Department of Mathematics and Statistics FI-40014 University of Jyvaskyla, Finland
Differentiable Structure Learning for Cyclic Linear Gaussian Models with Latent Confounders
Sep 29, 2026Sadegh Khorasani, Ali Najar, Saber Salehkaleybar +1Structural Causal Models Causal Discovery
School of Computer and Communication Sciences, EPFL, Lausanne, Switzerland · Leiden Institute of Advanced Computer Science (LIACS), Leiden University, Leiden, The Netherlands · College of Management of Technology, EPFL, Lausanne, Switzerland
Learning Causal Normalizing Flows from Incomplete Data via Observed-Data Likelihood
Sep 29, 2026Trung-Dung Hoang, Alceu Bissoto, Tim Flühmann +4Normalizing Flows Learning with Missing Data
Department of Digital Medicine, University of Bern, Switzerland · Department of Diabetes, Endocrinology, Nutritional Medicine and Metabolism (UDEM), Inselspital, Bern University Hospital, University of Bern, Switzerland · Graduate School for Cellular and Biomedical Sciences (GCB), University of Bern, Switzerland +3
What You Observe Determines How You Identify Causal Effects: Evaluating Causal Models across Observational Views
Sep 29, 2026Heejin Jung, Gyeongdeok Seo, Hoyoon Byun +2Causal Effect Estimation Causal Foundation Models
Yonsei University · University of Illinois Urbana–Champaign
Beyond Conditional Independence: Root Cause Analysis with Deep Causal Models
Sep 29, 2026Md Musfiqur Rahman, Kenneth Lee, Ziwei Jiang +3Structural Causal Models Causal Inference
Electrical and Computer Engineering, Purdue University · Computer Science, Johns Hopkins University · Intuit AI Research +1
CAFE: Counterfactual Prediction via Fast Posterior Estimation
Sep 26, 2026Xinyan Han, Xiaoyu Lin, Hao Zou +3Counterfactual Prediction Structural Causal Models
Tsinghua University · Stable AI
FOCUS: Fixed-Confidence Online Causal Learning Using Sequential Adaptive Interventions
Sep 26, 2026Haijie Xu, Chen ZhangCausal Discovery Structural Causal Models
Department of Industrial Engineering Tsinghua University Beijing, China
MARCEDES: Score-based causal discovery under non-Gaussianity with continuous optimization
Sep 25, 2026Anamitra Chaudhuri, Anirban Bhattacharya, Yang NiCausal Discovery Structural Causal Models
Department of Statistics Florida State University Tallahassee, FL 32306 · Department of Statistics The University of Texas at Austin · Department of Statistics Texas A&M University College Station, TX 77843 +1
ITSY: Causal Discovery From Irregular Time-Series Data
Sep 20, 2026Wenbo Xu, Yue He, Yunhai Wang +2Irregular Time-Series Modeling Structural Causal Models
Renmin University of China, China · Zhejiang University, China
Causal Inference with Unobserved Confounding: A Mixture Learning Perspective
Sep 19, 2026Mansi Sood, Devavrat ShahCausal Inference with Unobserved Confounding Causal Inference
Laboratory for Information and Decision Systems, Massachusetts Institute of Technology, Cambridge, MA 02139 USA
Epidemiological Causal Graph Identification: Challenges, Identifiability and Algorithms
Sep 17, 2026Sambit Mishra, Yingying Wang, Christine K. Johnson +1Structural Causal Models Causal Discovery
University of Southern California · University of California, Davis
Provable Guarantees and Efficient Learning of Structural Equation Models with Latent Confounders
Sep 16, 2026Weijian Yu, Jean HonorioStructural Causal Models Low-Rank Matrix Decomposition
CIS, The University of Melbourne
Active Causal Discovery Benchmark: Evaluating LLM Agents Under Budgeted Interventions
Sep 16, 2026Sagar Deb, Devam Shah, Ashwanth KrishnanCausal Discovery LLM Agent Evaluation
QpiAI
On the Identifiability of Mixed Ordinal and Exponential Family Causal DAGs under Linear Parametric Models
Sep 16, 2026Sambit Mishra, Urbashi MitraStructural Causal Models Causal Discovery
Ming Hsieh Department of Electrical and Computer Engineering University of Southern California Los Angeles, CA 90089, USA
CausalArena: Benchmarking Causal Discovery in the Foundation Model Era
Sep 10, 2026Zi-Rong Li, Si-Yang Liu, Tian-Zuo Wang +1Causal Foundation Models Structural Causal Models
School of Artificial Intelligence, Nanjing University
SeaCausal-FL: Federated Fuzzy Causal Learning for Maritime IoT Fault Diagnosis and Counterfactual Reasoning
Sep 5, 2026Yuhang Qiu, Haihan Zhu, Koteeswaran Seerangan +6Fault Diagnosis Industrial Fault Diagnosis
School of Science, Jimei University, Xiamen 361021, China · School of Artificial Intelligence, Hebei University of Technology, Tianjin 300401, China · Department of Computer Science and Engineering, R. M. K. Engineering College (Autonomous), Kavaraipettai - 601206, Tamil Nadu, India +4
Causal DAG Identification for Count Data via Poisson Thinning Structural Equation Models
Sep 5, 2026Penggang Gao, Ming Cai, Hisayuki HaraStructural Causal Models Causal Discovery
Graduate School of Informatics Kyoto University Kyoto, 606-8501, Japan · Institute for Liberal Arts and Sciences Kyoto University Kyoto, 606-8501, Japan
A Computationally Feasible Framework for Causal Probabilistic Explanation
Sep 3, 2026Rafal Urbaniak, Sam Witty, Daniel Waxman +7Structural Causal Models Actual Causality
Basis Research Institute · Sorbus AI · Massachusetts Institute of Technology +3
Federated Causal Discovery via Regression-Directed Cumulants
Sep 3, 2026Pablo Torrijos, Fabio Stella, José A. Gámez +1Structural Causal Models Causal Discovery
Departamento de Sistemas Inform´aticos Universidad de Castilla-La Mancha Albacete, Spain · Dipartimento di Informatica, Sistemistica e Comunicazione Universit`a degli Studi di Milano Bicocca Milano, Italy · Departamento de Sistemas Inform´aticos2026 Universidad de Castilla-La Mancha Albacete, Spain

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Towards Realistic Models of Change: A Causal Perspective on Decision-Making and Dynamical Systems

Isacco Beretta

Università di Pisa National Ph.D. in Artificial Intelligence XXXVII cycle

Causal Inference Algorithmic Recourse Structural Causal Models

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While statistical learning has made remarkable progress in recent decades, driven by large datasets and data-driven protocols that have transformed scientific practice, it still faces a key limitation: it struggles to generalize beyond the data it has seen, often failing under distribution shift. Causality offers a promising solution: causal models focus on the underlying mechanisms of change rather than surface-level associations, enabling robust reasoning under external interventions. This allows systems not only to learn from data, but also to reason about hypothetical scenarios, adapt to new environments, and inform better decisions. This thesis explores how these approaches can inform each other, in two parts. The first part examines algorithmic recourse, that is, identifying which changes an individual can make to overturn an unfavorable decision. We propose an alternative perspective to the established framework, arguing for a more realistic cost function, a reformulation of the problem in causal terms, and the inclusion of a temporal dimension in policy evaluation. The second part focuses on dynamical systems, extending the notions of causal interventions and effects to discrete and continuous stochastic processes. We show that the equilibria of these processes, and how they change following an intervention, can be linked to Structural Causal Models (SCMs), viewed as compressed representations of the underlying temporal dynamics. We argue for the relevance of such temporal causal models, since in real-world scenarios actions take time to produce their effects. The common goal of both parts is practical: the utility, robustness, and trustworthiness of these models depend critically on their ability to realistically represent the underlying phenomena.