2026 Clark Glymour: Computer Science Researcher – H-Index, Publications & Awards

Discipline name	D-Index	World Ranking	Current World Ranking	National Ranking	Current National Ranking	Publications	Citations
Computer Science	56	3934	3823	1866	1799	231	33766

Research.com Recognitions

2019 - Fellow of the American Academy of Arts and Sciences
2004 - Fellow of the American Association for the Advancement of Science (AAAS)
1989 - Fellow of John Simon Guggenheim Memorial Foundation

Overview

Clark Glymour is affiliated with Carnegie Mellon University in the United States. Their research primarily focuses on the field of Computer Science, with a specialization in Artificial Intelligence. Additional areas of study include Management Science and Operations Research, Information Systems, Statistics and Probability, and Control and Systems Engineering.

The scientist's work covers several main topics, including:

Bayesian Modeling and Causal Inference
Machine Learning and Data Classification
Data Quality and Management
Domain Adaptation and Few-Shot Learning
Data Mining Algorithms and Applications
Statistical Methods and Inference
Multi-Criteria Decision Making

Clark Glymour has contributed multiple papers to notable publication venues. Frequent outlets for their research include:

arXiv (Cornell University)
Philosophy of Science
Proceedings of the AAAI Conference on Artificial Intelligence
Physics Today

Some of their recent papers are:

Domain Adaptation as a Problem of Inference on Graphical Models, 2020, arXiv (Cornell University)
Causal Discovery from Multiple Data Sets with Non-Identical Variable Sets, 2020, Proceedings of the AAAI Conference on Artificial Intelligence
Latent Hierarchical Causal Structure Discovery with Rank Constraints, 2022, arXiv (Cornell University)
Generalized Independent Noise Condition for Estimating Latent Variable Causal Graphs, 2020, arXiv (Cornell University)
Action-Sufficient State Representation Learning for Control with Structural Constraints, 2021, arXiv (Cornell University)

Frequent co-authors working with Clark Glymour include:

Biwei Huang
Kun Zhang
Ruichu Cai
Mingming Gong
Feng Xie

Clark Glymour has been recognized with several professional fellowships, including:

Fellow of the American Academy of Arts and Sciences, 2019
Fellow of the American Association for the Advancement of Science (AAAS), 2004
Fellow of John Simon Guggenheim Memorial Foundation, 1989

Best Publications

Causation, prediction, and search

Peter Spirtes;Clark N. Glymour;Richard Scheines

9182
Citations
Causation, Prediction, and Search, 2nd Edition

Peter Spirtes;Clark Glymour;Richard Scheines

9053
Citations
A Theory of Causal Learning in Children: Causal Maps and Bayes Nets.

Alison Gopnik;Clark Glymour;David M. Sobel;Laura E. Schulz

1715
Citations
An Algorithm for Fast Recovery of Sparse Causal Graphs

Peter Spirtes;Clark N. Glymour

1147
Citations
Inferring causation from time series in Earth system sciences

Jakob Runge;Jakob Runge;Sebastian Bathiany;Erik Bollt;Gustau Camps-Valls

862
Citations
Review of Causal Discovery Methods Based on Graphical Models.

Clark Glymour;Kun Zhang;Peter Spirtes

798
Citations
Causal learning mechanisms in very young children: two-, three-, and four-year-olds infer causal relations from patterns of variation and covariation.

Alison Gopnik;David M. Sobel;Laura E. Schulz;Clark Glymour

715
Citations
Discovering Causal Structure: Artificial Intelligence, Philosophy of Science, and Statistical Modeling

Clark Glymour;Richard Scheines;Peter Spirtes;Kevin T. Kelly

702
Citations
Computation, Causation, and Discovery

Clark N. Glymour;Gregory Floyd Cooper

508
Citations
Six problems for causal inference from fMRI

Joseph D. Ramsey;Stephen José Hanson;Catherine Hanson;Yaroslav O. Halchenko

415
Citations
Statistical Themes and Lessons for Data Mining

Clark Glymour;David Madigan;Daryl Pregibon;Padhraic Smyth

376
Citations
The Mind's Arrows: Bayes Nets and Graphical Causal Models in Psychology

Clark N. Glymour

367
Citations
A million variables and more: the Fast Greedy Equivalence Search algorithm for learning high-dimensional graphical causal models, with an application to functional magnetic resonance images

Joseph D. Ramsey;Madelyn Glymour;Ruben Sanchez-Romero;Clark Glymour

346
Citations
Discovering Causal Structure.

S. C. Pearce;C. Glymour;R. Scheines;P. Spirtes

306
Citations
The TETRAD project: Constraint based aids to causal model specification.

Richard Scheines;Peter Spirtes;Clark Glymour;Christopher Meek

292
Citations
Causal inference

Unknown

292
Citations
Preschool children learn about causal structure from conditional interventions.

Laura E. Schulz;Alison Gopnik;Clark Glymour

290
Citations
An Evaluation of Machine-Learning Methods for Predicting Pneumonia Mortality

Gregory F. Cooper;Constantin F. Aliferis;Richard Ambrosino;John M. Aronis

287
Citations
Domain adaptation with conditional transferable components

Mingming Gong;Kun Zhang;Tongliang Liu;Dacheng Tao

264
Citations
Optical coherence tomography machine learning classifiers for glaucoma detection: a preliminary study

Zvia Burgansky-Eliash;Gadi Wollstein;Tianjiao Chu;Joseph D. Ramsey

252
Citations
Learning the Structure of Linear Latent Variable Models

Ricardo Silva;Richard Scheines;Clark Glymour;Peter Spirtes

228
Citations

Frequent Co-Authors

Peter Spirtes Carnegie Mellon University

Richard Scheines Carnegie Mellon University

Kun Zhang Carnegie Mellon University

Bernhard Schölkopf Max Planck Institute for Intelligent Systems

John Earman University of Pittsburgh

Alison Gopnik University of California, Berkeley

Thomas S. Richardson University of Washington

Padhraic Smyth University of California, Irvine

David Madigan Northeastern University

Laura Schulz MIT

External Links

Wikipedia of Clark Glymour Personal website of Clark Glymour

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