Clark Glymour
Carnegie Mellon University
273 Papers
2.7K Citations
Clark Glymour is an academic researcher from Carnegie Mellon University. The author has contributed to research in topics: Causal model & Causal structure. The author has an hindex of 47, co-authored 268 publications. Previous affiliations of Clark Glymour include Florida Institute for Human and Machine Cognition & University of West Florida.
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Papers
Inferring causation from time series in Earth system sciences
Jakob Runge,Jakob Runge,Sebastian Bathiany,Erik M. Bollt,Gustau Camps-Valls,Dim Coumou,Dim Coumou,Ethan R. Deyle,Clark Glymour,Marlene Kretschmer,Miguel D. Mahecha,Jordi Muñoz-Marí,Egbert H. van Nes,Jonas Peters,Rick Quax,Markus Reichstein,Marten Scheffer,Bernhard Schölkopf,Peter Spirtes,George Sugihara,Jie Sun,Kun Zhang,Jakob Zscheischler,Jakob Zscheischler,Jakob Zscheischler +24 more
TL;DR: An overview of causal inference frameworks is given, promising applications and methodological challenges are identified, and a causality benchmark platform is initiated to close the gap between method users and developers.
An Evaluation of Machine-Learning Methods for Predicting Pneumonia Mortality
Gregory F. Cooper,Constantin F. Aliferis,Richard Ambrosino,John M. Aronis,Bruce G. Buchanan,Rich Caruana,Michael J. Fine,Clark Glymour,Geoffrey J. Gordon,Barbara H. Hanusa,Janine E. Janosky,Christopher Meek,Tom M. Mitchell,Thomas S. Richardson,Peter Spirtes +14 more
TL;DR: The models are distinguished more by the number of variables and parameters that they contain than by their error rates; these differences suggest which models may be the most amenable to future implementation as paper-based guidelines.
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Causation, Prediction, and Search, 2nd Edition
Peter Spirtes,Clark Glymour,Richard Scheines +2 more
- 01 Jan 2001
TL;DR: The authors axiomatize the connection between causal structure and probabilistic independence, explore several varieties of causal indistinguishability, formulate a theory of manipulation, and develop asymptotically reliable procedures for searching over equivalence classes of causal models.
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Causal learning mechanisms in very young children: two-, three-, and four-year-olds infer causal relations from patterns of variation and covariation.
TL;DR: Three studies investigated whether young children make accurate causal inferences on the basis of patterns of variation and covariation between two different objects and the activation of the machine.