If quanta had logic. - Michael Friedman; Clark Glymour (J. Philos. Log., 1972)

Default Reasoning and the Logic of Theory Perturbation. - Clark Glymour; Richmond H. Thomason (NMR, 1984)

Independence Assumptions and Bayesian Updating. - Clark Glymour (Artif. Intell., 1985)

Theory discovery from data with mixed quantifiers. - Kevin T. Kelly; Clark Glymour (J. Philos. Log., 1990)

The hierarchies of knowledge and the mathematics of discovery. - Clark Glymour (Minds Mach., 1991)

Inductive inference from theory laden data. - Kevin T. Kelly; Clark Glymour (J. Philos. Log., 1992)

Application of the TETRAD II Program to the Study of Student Retention in U.S. Colleges. - Marek J. Druzdze; Clark Glymour (KDD Workshop, 1994)

Available Technology for Discovering Causal Models, Building Bayes Nets, and Selecting Predictors: The TETRAD II Program. - Clark Glymour (KDD, 1995)

Statistical Inference and Data Mining. - Clark Glymour; David Madigan; Daryl Pregibon; Padhraic Smyth (Commun. ACM, 1996)

A Note on Cyclic Graphs and Dynamical Feedback Systems. - Thomas S. Richardson 001; Peter Spirtes; Clark Glymour (AISTATS, 1997)

On the Other Hand - Cognitive Prostheses. - Kenneth M. Ford; Clark Glymour; Patrick J. Hayes (AI Mag., 1997)

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; et al. (Artif. Intell. Medicine, 1997)

Statistical Themes and Lessons for Data Mining. - Clark Glymour; David Madigan; Daryl Pregibon; Padhraic Smyth (Data Min. Knowl. Discov., 1997)

Psychological and Normative Theories of Causal Power and the Probabilities of Causes. - Clark Glymour (UAI, 1998)

Ramón Lull and the Infidels. - Clark Glymour; Kenneth M. Ford; Patrick J. Hayes (AI Mag., 1998)

Learning Causes: Psychological Explanations of Causal Explanation. - Clark Glymour (Minds Mach., 1998)

Buy and Use Thinking Things Through. - Clark Glymour (Minds Mach., 1998)

Rabbit Hunting. - Clark Glymour (Synth., 1999)

Causation, Prediction, and Search, Second Edition. - Peter Spirtes; Clark Glymour; Richard Scheines (MIT Press, 2000)

Android Epistemology For Babies: Relections On Words, Thoughts And Theories. - Clark Glymour (Synth., 2000)

Linearity Properties of Bayes Nets with Binary Variables. - David Danks; Clark Glymour (UAI, 2001)

Automated Remote Sensing with Near Infrared Reflectance Spectra: Carbonate Recognition. - Joseph D. Ramsey; Paul Gazis; Ted Roush; Peter Spirtes; Clark Glymour (Data Min. Knowl. Discov., 2002)

Classification and filtering of spectra: A case study in mineralogy. - Jonathan Moody; Ricardo Bezerra de Andrade e Silva; Joseph Vanderwaart; Joseph D. Ramsey; Clark Glymour (Intell. Data Anal., 2002)

Learning Measurement Models for Unobserved Variables. - Ricardo Bezerra de Andrade e Silva; Richard Scheines; Clark Glymour; Peter Spirtes (UAI, 2003)

A Statistical Problem for Inference to Regulatory Structure from Associations of Gene Expression Measurements with Microarrays. - Tianjiao Chu; Clark Glymour; Richard Scheines; Peter Spirtes (Bioinform., 2003)

On the Number of Experiments Sufficient and in the Worst Case Necessary to Identify All Causal Relations Among N Variables. - Frederick Eberhardt; Clark Glymour; Richard Scheines (UAI, 2005)

Learning the Structure of Linear Latent Variable Models. - Ricardo Bezerra de Andrade e Silva; Richard Scheines; Clark Glymour; Peter Spirtes (J. Mach. Learn. Res., 2006)

Trade-Offs. - Clark Glymour (Induction, Algorithmic Learning Theory, and Philosophy, 2007)

Integrating Locally Learned Causal Structures with Overlapping Variables. - Robert E. Tillman; David Danks; Clark Glymour (NIPS, 2008)

Search for Additive Nonlinear Time Series Causal Models. - Tianjiao Chu; Clark Glymour (J. Mach. Learn. Res., 2008)

Using Causal Modeling for Determining Connectivity among Brain Regions. - Eman El-Sheikh; Carlos Perez; Clark Glymour (IC-AI, 2010)

Discovering effective connectivity among brain regions from functional MRI data. - Carlos Perez; Eman El-Sheikh; Clark Glymour (Int. J. Comput. Heal., 2010)

Six problems for causal inference from fMRI. - Joseph D. Ramsey; Stephen José Hanson; Catherine Hanson; Yaroslav O. Halchenko; Russell A. Poldrack; Clark Glymour (NeuroImage, 2010)

Actual causation: a stone soup essay. - Clark Glymour; David Danks; Bruce Glymour; Frederick Eberhardt; Joseph D. Ramsey; Richard Scheines; Peter Spirtes; Choh Man Teng 001; Jiji Zhang (Synth., 2010)

Multi-subject search correctly identifies causal connections and most causal directions in the DCM models of the Smith et al. simulation study. - Joseph D. Ramsey; Stephen José Hanson; Clark Glymour (NeuroImage, 2011)

On meta-analyses of imaging data and the mixture of records. - Joseph D. Ramsey; Peter Spirtes; Clark Glymour (NeuroImage, 2011)

Hans Reichenbach's Probability Logic. - Frederick Eberhardt; Clark Glymour (Inductive Logic, 2011)

On the Possibility of Inference to the Best Explanation. - Clark Glymour (J. Philos. Log., 2012)

Atypical Effective Connectivity of Social Brain Networks in Individuals with Autism. - Catherine Hanson; Stephen José Hanson; Joseph D. Ramsey; Clark Glymour (Brain Connect., 2013)

Counterfactuals, graphical causal models and potential outcomes: Response to Lindquist and Sobel. - Clark Glymour (NeuroImage, 2013)

Non-Gaussian methods and high-pass filters in the estimation of effective connections. - Joseph D. Ramsey; Ruben Sanchez-Romero; Clark Glymour (NeuroImage, 2014)

Cognitive Orthoses: Toward Human-Centered AI. - Kenneth M. Ford; Patrick J. Hayes; Clark Glymour; James F. Allen (AI Mag., 2015)

The center for causal discovery of biomedical knowledge from big data. - Gregory F. Cooper; Ivet Bahar; Michael J. Becich; Panayiotis V. Benos; Jeremy M. Berg; Jeremy U. Espino; Clark Glymour; Rebecca Crowley Jacobson; Michelle Kienholz; Adrian V. Lee; Xinghua Lu 001; Richard Scheines (J. Am. Medical Informatics Assoc., 2015)

Domain Adaptation with Conditional Transferable Components. - Mingming Gong; Kun Zhang 001; Tongliang Liu; Dacheng Tao; Clark Glymour; Bernhard Schölkopf (ICML, 2016)

On the Identifiability and Estimation of Functional Causal Models in the Presence of Outcome-Dependent Selection. - Kun Zhang 001; Jiji Zhang; Biwei Huang; Bernhard Schölkopf; Clark Glymour (UAI, 2016)

Clark Glymour's responses to the contributions to the Synthese special issue "Causation, probability, and truth: the philosophy of Clark Glymour". - Clark Glymour (Synth., 2016)

Behind Distribution Shift: Mining Driving Forces of Changes and Causal Arrows. - Biwei Huang; Kun Zhang 001; Jiji Zhang; Ruben Sanchez-Romero; Clark Glymour; Bernhard Schölkopf (ICDM, 2017)

Causal Discovery from Nonstationary/Heterogeneous Data: Skeleton Estimation and Orientation Determination. - Kun Zhang 001; Biwei Huang; Jiji Zhang; Clark Glymour; Bernhard Schölkopf (IJCAI, 2017)

Causal Discovery from Temporally Aggregated Time Series. - Mingming Gong; Kun Zhang 001; Bernhard Schölkopf; Clark Glymour; Dacheng Tao (UAI, 2017)

Mixed Graphical Models for Causal Analysis of Multi-modal Variables. - Andrew J. Sedgewick; Joseph D. Ramsey; Peter Spirtes; Clark Glymour; Panayiotis V. Benos (CoRR, 2017)

Causal Discovery in the Presence of Measurement Error: Identifiability Conditions. - Kun Zhang 001; Mingming Gong; Joseph D. Ramsey; Kayhan Batmanghelich; Peter Spirtes; Clark Glymour (CoRR, 2017)

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 (Int. J. Data Sci. Anal., 2017)

Generalized Score Functions for Causal Discovery. - Biwei Huang; Kun Zhang 001; Yizhu Lin; Bernhard Schölkopf; Clark Glymour (KDD, 2018)

Causal Discovery with Linear Non-Gaussian Models under Measurement Error: Structural Identifiability Results. - Kun Zhang 001; Mingming Gong; Joseph D. Ramsey; Kayhan Batmanghelich; Peter Spirtes; Clark Glymour (UAI, 2018)

Causal Generative Domain Adaptation Networks. - Mingming Gong; Kun Zhang 001; Biwei Huang; Clark Glymour; Dacheng Tao; Kayhan Batmanghelich (CoRR, 2018)

Comparison of strategies for scalable causal discovery of latent variable models from mixed data. - Vineet K. Raghu; Joseph D. Ramsey; Alison Morris; Dimitrios V. Manatakis; Peter Spirtes; Panos K. Chrysanthis; Clark Glymour; Panayiotis V. Benos (Int. J. Data Sci. Anal., 2018)

Causal Discovery and Forecasting in Nonstationary Environments with State-Space Models. - Biwei Huang; Kun Zhang 001; Mingming Gong; Clark Glymour (ICML, 2019)

Triad Constraints for Learning Causal Structure of Latent Variables. - Ruichu Cai; Feng Xie 002; Clark Glymour; Zhifeng Hao; Kun Zhang 001 (NeurIPS, 2019)

Specific and Shared Causal Relation Modeling and Mechanism-Based Clustering. - Biwei Huang; Kun Zhang 001; Pengtao Xie; Mingming Gong; Eric P. Xing; Clark Glymour (NeurIPS, 2019)

Mixed graphical models for integrative causal analysis with application to chronic lung disease diagnosis and prognosis. - Andrew J. Sedgewick; Kristina Buschur; Ivy Shi; Joseph D. Ramsey; Vineet K. Raghu; Dimitris V. Manatakis; Yingze Zhang; Jessica Bon; Divay Chandra; Chad Karoleski; Frank C. Sciurba; Peter Spirtes; et al. (Bioinform., 2019)

Diagnosis of Autism Spectrum Disorder by Causal Influence Strength Learned from Resting-State fMRI Data. - Biwei Huang; Kun Zhang 001; Ruben Sanchez-Romero; Joseph D. Ramsey; Madelyn Glymour; Clark Glymour (CoRR, 2019)

Identification of Effective Connectivity Subregions. - Ruben Sanchez-Romero; Joseph D. Ramsey; Kun Zhang 001; Clark Glymour (CoRR, 2019)

Causal Discovery from Multiple Data Sets with Non-Identical Variable Sets. - Biwei Huang; Kun Zhang 001; Mingming Gong; Clark Glymour (AAAI, 2020)

Domain Adaptation as a Problem of Inference on Graphical Models. - Kun Zhang 001; Mingming Gong; Petar Stojanov; Biwei Huang; Qingsong Liu; Clark Glymour (NeurIPS, 2020)

Generalized Independent Noise Condition for Estimating Latent Variable Causal Graphs. - Feng Xie 002; Ruichu Cai; Biwei Huang; Clark Glymour; Zhifeng Hao; Kun Zhang 001 (NeurIPS, 2020)

Domain Adaptation As a Problem of Inference on Graphical Models. - Kun Zhang 001; Mingming Gong; Petar Stojanov; Biwei Huang; Clark Glymour (CoRR, 2020)

Generalized Independent Noise Condition for Estimating Linear Non-Gaussian Latent Variable Graphs. - Feng Xie 002; Ruichu Cai; Biwei Huang; Clark Glymour; Zhifeng Hao; Kun Zhang 001 (CoRR, 2020)

Causal Discovery from Heterogeneous/Nonstationary Data. - Biwei Huang; Kun Zhang 001; Jiji Zhang; Joseph D. Ramsey; Ruben Sanchez-Romero; Clark Glymour; Bernhard Schölkopf (J. Mach. Learn. Res., 2020)

FRITL: A Hybrid Method for Causal Discovery in the Presence of Latent Confounders. - Wei Chen 103; Kun Zhang 001; Ruichu Cai; Biwei Huang; Joseph D. Ramsey; Zhifeng Hao; Clark Glymour (CoRR, 2021)

Action-Sufficient State Representation Learning for Control with Structural Constraints. - Biwei Huang; Chaochao Lu; Liu Leqi; José Miguel Hernández-Lobato; Clark Glymour; Bernhard Schölkopf; Kun Zhang 001 (ICML, 2022)

Latent Hierarchical Causal Structure Discovery with Rank Constraints. - Biwei Huang; Charles Jia Han Low; Feng Xie 002; Clark Glymour; Kun Zhang 001 (NeurIPS, 2022)

Generalized Independent Noise Condition for Estimating Causal Structure with Latent Variables. - Feng Xie 002; Biwei Huang; Zhengming Chen; Ruichu Cai; Clark Glymour; Zhi Geng; Kun Zhang 001 (J. Mach. Learn. Res., 2024)