Linear Structural Equation Models

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3 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

Jul 13Week of Sep 28

Latest papers 19

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Epidemiological Causal Graph Identification: Challenges, Identifiability and Algorithms
Sep 17, 2026Sambit Mishra, Yingying Wang, Christine K. Johnson +1Causal Discovery Methods Causal Graph
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 HonorioLinear Structural Equation Models Latent Confounders
CIS, The University of Melbourne
On the Identifiability of Mixed Ordinal and Exponential Family Causal DAGs under Linear Parametric Models
Sep 16, 2026Sambit Mishra, Urbashi MitraLinear Structural Equation Models Identifiability
Ming Hsieh Department of Electrical and Computer Engineering University of Southern California Los Angeles, CA 90089, USA
Learning Bidirectional Causal Interactions with Heteroscedastic Neural Networks
Jul 24, 2026Masahiro TanakaLinear Structural Equation Models Heteroskedasticity
Faculty of Economics, Fukuoka University, Fukuoka, Japan
Public perceptions of AI-driven decision-making in healthcare: A structural equation modeling approach
Jul 21, 2026Leonie Westerbeek, Ernesto de Leon, Julia C. M. van WeertHealthcare Decisions
Amsterdam School of Communication Research/ASCoR, University of Amsterdam, Amsterdam, The Netherlands
Global drivers and barriers to the public acceptance of autonomous vehicles: Evidence from 17 countries
Jul 16, 2026Antonios SaravanosAutomated Vehicles Acceptance
New York University · Division of Applied Undergraduate Studies, New York University, New York, NY, USA
From Structural Equation Modelling to Double Machine Learning: Robustness Analysis for Survey-Based Research
Jul 1, 2026Ka Ching Chan, Qiana Liu, Sanjib Tiwari +1Linear Structural Equation Models Robustness Verification
School of Business, Law, Humanities and Pathways, University of Southern Queensland, Springfield, 4300, Queensland, Australia
A causal modeling perspective on decision theory
Jun 29, 2026Arvid SjölanderCausal Modeling Decisions
Karolinska Institutet
RetiSEM: Generalising Causal Models for Fragmented Biomedical Data
Jun 23, 2026Inam Ullah, Imran Razzak, Shoaib JameelCausal Modeling Linear Structural Equation Models
University of Southampton, Southampton, SO18 1PB, Hampshir, United Kingdom · Mohamed bin Zayed University of Artificial Intelligence, United Arab Emirates
Bayesian Networks with Latent Time Embedding for Stage-Aware Causal Modeling of Alzheimer's Disease Progression
Jun 14, 2026Nguyen Linh Dan LeAlzheimer Bayesian Networks
Department of Biomedical Engineering The University of Melbourne, Melbourne, Australia
Formalizing and falsifying causal pathways of rare events
May 29, 2026Anahita Haghighat, Dominik JanzingCausal Modeling Rare Events
Independent · Amazon Research, Tübingen, Germany
GUT-IS: A Data-Driven Approach to Integrating Constructs and Their Relations in Information Systems
May 18, 2026Maximilian Reinhardt, Jonas Scharfenberger, Burkhardt FunkLinear Structural Equation Models Semantic Relationships
Institute of Information Systems Leuphana University Lüneburg, Germany
Relaxed Sparsest-Permutation Formulation for Causal Discovery at Scale
May 7, 2026Sunmin Oh, Sang-Yun Oh, Gunwoong ParkCausal Discovery Methods Linear Structural Equation Models
Department of Statistics, Seoul National University, South Korea · Department of Statistics and Applied Probability, University of California Santa Barbara, CA, USA · Scientific Data Division, Lawrence Berkeley National Laboratory, CA, USA +2
Heterogeneous Ordinal Structure Learning with Bayesian Nonparametric Complexity Discovery
May 5, 2026Amir Rafe, Subasish DasLinear Structural Equation Models Bayesian Networks
Texas State University San Marcos, USA
Covariance-Based Structural Equation Modeling in Small-Sample Settings with $p>n$
Apr 18, 2026Hiroki Hasegawa, Aoba Tamura, Yukihiko OkadaLinear Structural Equation Models Covariance
Graduate School of Science and Technology, University of Tsukuba, Tsukuba, Ibaraki, Japan · Institute of Systems and Information Engineering, University of Tsukuba, Tsukuba, Ibaraki, Japan · Tsukuba Institute for Advanced Research, University of Tsukuba, Tsukuba, Ibaraki, Japan +1
Value-Action Alignment in Large Language Models under Privacy-Prosocial Conflict
Jan 7, 2026Guanyu Chen, Chenxiao Yu, Xiyang HuLarge Language Model Alignment Human Values
Arizona State University · University of Southern California
Root Cause Analysis of Outliers in Unknown Cyclic Graphs
Oct 8, 2025Daniela Schkoda, Dominik JanzingCausal Graph Root Cause Analysis
Amazon Research Tübingen, Germany · Technical University of Munich, Germany
Multi-View Causal Discovery without Non-Gaussianity: Identifiability and Algorithms
Feb 27, 2025Ambroise Heurtebise, Omar Chehab, Pierre Ablin +2Causal Discovery Methods Linear Structural Equation Models
Inria, CEA, University of Paris-Saclay, France · University of Carnegie Mellon, Pittsburgh PA, USA · Apple +1
Causal Discovery in Mixtures of Populations
Nov 13, 2023Bijan Mazaheri, Spencer Gordon, Yuval Rabani +1Causal Discovery Methods Linear Structural Equation Models
Thayer School of Engineering, Dartmouth College, Hanover, New Hampshire, USA · Fragment Data Technologies, San Francisco, California, USA · Department of Computer Science, Hebrew Univeristy of Jerusalem, Jerusalem, Israel +1

Sep 17, 2026, cs.LGJ/K move · Enter open · S save

Epidemiological Causal Graph Identification: Challenges, Identifiability and Algorithms

Sambit Mishra, Yingying Wang, Christine K. Johnson, Urbashi Mitra

University of Southern California · University of California, Davis

Causal Discovery Methods Causal Graph Linear Structural Equation Models Directed Acyclic Graph Identifiability Distributions+1

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Causal discovery from observational data is fundamental to statistics and machine learning, yet determining causal direction without interventions necessitates structural assumptions. Existing identifiability research primarily focuses on continuous variables under additive noise models, often neglecting mixed datasets containing ordinal scales, counts, and continuous measurements. This paper investigates causal discovery in Directed Acyclic Graphs (DAGs) where nodes follow either an ordinal distribution (via an ordered logit model) or a regular one-parameter exponential family distribution. We prove that the edge direction between an ordinal and an exponential family node is distributionally identifiable for generic parameter values. Our findings generalize previous Ordinal-Poisson results to the broader exponential family. Computationally, we introduce a score-based exhaustive search and a masked continuous optimization framework using DAGMA for larger graphs. Numerical results validate the theory, recovering edge orientations within a Markov equivalence class that are unidentifiable under classical structural equation models.

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