C. Glymour

发表

Causation, prediction, and search

P. Spirtes, C. Glymour, R. Scheines, 1993 .

Discovering Causal Structure.

Kevin T. Kelly, P. Spirtes, C. Glymour, 1989 .

An Algorithm for Fast Recovery of Sparse Causal Graphs

P. Spirtes, C. Glymour, 1991 .

Causality From Probability

P. Spirtes, C. Glymour, R. Scheines, 1989 .

A theory of causal learning in children: causal maps and Bayes nets.

David M. Sobel, C. Glymour, A. Gopnik, 2004, Psychological review.

Causation, Prediction, and Search, 2nd Edition

P. Spirtes, C. Glymour, R. Scheines, 2001 .

Conditioning and Intervening

C. Glymour, Christopher Meek, 1994, The British Journal for the Philosophy of Science.

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

C. Glymour, 2000 .

Domain Adaptation with Conditional Transferable Components

Bernhard Schölkopf, Dacheng Tao, Mingming Gong, 2016, ICML.

On the theoretical limits to reliable causal inference

C. Glymour, B. Desjardins, 1999 .

Causation, Prediction, and Search, Second Edition

Richard Scheines, Clark Glymour, Peter Spirtes, 2000, Adaptive computation and machine learning.

Preschool children learn about causal structure from conditional interventions.

C. Glymour, A. Gopnik, L. Schulz, 2007, Developmental science.

Causal Inference and Causal Explanation

C. Glymour, 1982 .

Computation, causation, and discovery

Mtw, C. Glymour, Gregory F. Copper, 2000 .

Statistical inference and data mining

Padhraic Smyth, Clark Glymour, David Madigan, 1996, CACM.

STATISTICS AND CAUSAL INFERENCE

C. Glymour, P. Holland, C. Granger, 1985 .

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

David M. Sobel, C. Glymour, A. Gopnik, 2001, Developmental psychology.

Using Path Diagrams as a Structural Equation Modeling Tool

P. Spirtes, C. Glymour, R. Scheines, 1998 .

Latent variables, causal models and overidentifying constraints

P. Spirtes, C. Glymour, 1988 .

Causal structure among measured variables preserved with unmeasured variables

P. Spirtes, C. Glymour, 1990 .

Android epistemology

Kenneth M. Ford, Patrick J. Hayes, Clark Glymour, 1995 .

From probability to causality

P. Spirtes, C. Glymour, R. Scheines, 1991 .

Specific and Shared Causal Relation Modeling and Mechanism-Based Clustering

Pengtao Xie, Eric P. Xing, Kun Zhang, 2019, NeurIPS.

A Statistical Problem for Inference to Regulatory Structure from Associations of Gene Expression Measurements with Microarrays

Richard Scheines, Clark Glymour, Peter Spirtes, 2003, Bioinform..

Commentary: race and sex are causes.

C. Glymour, M. Glymour, Madelyn Glymour, 2014, Epidemiology.

Markov Properties and Quantum Experiments

C. Glymour, 2006 .

Relativity and Eclipses: The British Eclipse Expeditions of 1919 and Their Predecessors

C. Glymour, J. Earman, 1980 .

Invasion of the Mind Snatchers

C. Glymour, 1992 .

Data Driven Methods for Nonlinear Granger Causality: Climate Teleconnection Mechanisms

C. Glymour, D. Danks, T. Chu, 2005 .

Independence Assumptions and Bayesian Updating

Clark Glymour, C. Glymour, 1985, Artif. Intell..

Review of Causal Discovery Methods Based on Graphical Models

P. Spirtes, C. Glymour, Kun Zhang, 2019, Front. Genet..

Simulation Studies of the Reliability of Computer-Aided Model Specification Using the TETRAD II, EQS, and LISREL Programs

Richard Scheines, Clark Glymour, Peter Spirtes, 1990 .

Social science and social physics

C. Glymour, 1983 .

Causal maps and Bayes nets: A cognitive and computational account of theory-formation

C. Glymour, A. Gopnik, 2002 .

Clarifying the locality assumption

C. Glymour, 1994 .

Scale-Free Exponents of Resting State provide a Biomarker for Typical and Atypical Brain Activity

C. Glymour, S. Hanson, C. Hanson, 2016, 1605.09282.

Comment: Statistics and Metaphysics

C. Glymour, 1986 .

Statistics and Causal Inference: Comment: Statistics and Metaphysics

C. Glymour, 1986 .

Inference, Intervention, and Prediction

P. Spirtes, C. Glymour, 1994 .

Prediction and Experimental Design with Graphical Causal Models

P. Spirtes, C. Glymour, R. Scheines, 1992 .

Theoretical Equivalence and the Semantic View of Theories

C. Glymour, 2013, Philosophy of Science.

The Epistemology of Geometry

C. Glymour, 1977 .

Explanations, Tests, Unity and Necessity

C. Glymour, 1980 .

Causal Generative Domain Adaptation Networks pdf

Dacheng Tao, Mingming Gong, Clark Glymour, 2018, ArXiv.

Causal Discovery and Hidden Driving Force Estimation from Nonstationary/Heterogeneous Data

C. Glymour, B. Schölkopf, Kun Zhang, 2019 .

Reliability, Realism, and Relativism

Kevin T. Kelly, C. Glymour, Cory Juhl, 1992 .

Learning, prediction and causal Bayes nets

C. Glymour, 2003, Trends in Cognitive Sciences.

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

C. Glymour, G. Wollstein, H. Ishikawa, 2005, Investigative ophthalmology & visual science.

Reasons as Causes in Bayesian Epistemology

C. Glymour, D. Danks, 2007 .

Causal Mechanism and Probability: A Normative Approach

C. Glymour, P. Cheng, 1998 .

Cognition and explanation

H. Simon, C. Glymour, A. Clark, 1998 .

Reply to Comments.

P. Spirtes, C. Glymour, T. Richardson, 1998, Multivariate behavioral research.

Reply to Humphreys and Freedman's Review of Causation, Prediction, and Search

P. Spirtes, C. Glymour, R. Scheines, 1997, The British Journal for the Philosophy of Science.

What Is Going on Inside the Arrows? Discovering the Hidden Springs in Causal Models

C. Glymour, Alexander Murray-Watters, 2015, Philosophy of Science.

What Is Right with ‘Bayes Net Methods’ and What Is Wrong with ‘Hunting Causes and Using Them’?

C. Glymour, 2010, The British Journal for the Philosophy of Science.

Using D-Separation to Calculate Zero Partial Correlations in Linear Models with Correlated Errors

Richard Scheines, Christopher Meek, Thomas S. Richardson, 1996 .

What Went Wrong? Reflections on Science by Observation and the Bell Curve

Clark Glymour, C. Glymour, 1998, Philosophy of Science.

Social Statistics and Genuine Inquiry: Reflections on The Bell Curve

Clark Glymour, C. Glymour, 1997 .

Fodor's holism

C. Glymour, 1985, Behavioral and Brain Sciences.

Thinking Things Through: An Introduction to Philosophical Issues and Achievements

C. Glymour, Clark N. Glymour, 1992 .

Complementing explanation with induction

C. Glymour, 1986, Behavioral and Brain Sciences.

Nonconsequentialist decisions. Commentaries. Author's reply

Jonathan Baron, Peter Ayton, Ilana Ritov, 1994 .

ESP and the Big Stuff

C. Glymour, 1987 .

Review of James Woodward, Making Things Happen: A Theory of Causal Explanation

Clark Glymour, C. Glymour, 2004 .

James Woodward, Making Things Happen: A Theory of Causal Explanation, Oxford, 2003, 418pp, $65.00 (hbk) ISBN 0195155270

Clark Glymour, C. Glymour, 2004 .

Material for “ Causal Discovery and Forecasts in Nonstationary Environments with State-Space Models ”

C. Glymour, Mingming Gong, Kun Zhang, 2019 .

What Language Dependence Problem? A Reply for Joyce to Fitelson on Joyce

Arthur Paul Pedersen, C. Glymour, 2012, Philosophy of Science.

Causal Discovery in the Presence of Missing Values for Neuropathic Pain Diagnosis

C. Glymour, H. Kjellström, Kun Zhang, 2020 .

When Is a Brain Like the Planet?*

C. Glymour, 2007, Philosophy of Science.

Chapter 24 Causal Inference

P. Spirtes, C. Glymour, R. Scheines, 2004 .

Getting to the Truth through Conceptual Revolutions

Kevin T. Kelly, C. Glymour, 1990, PSA: Proceedings of the Biennial Meeting of the Philosophy of Science Association.

Indistinguishable Space-Times and the Fundamental Group

C. Glymour, 1977 .

Learning causality and causality-related learning: some recent progress.

Bernhard Schölkopf, Clark Glymour, Kun Zhang, 2018, National science review.

Actual Causation : A Stone Soup Essay Draft of February 9 , 2007 Authors so far , if they are willing

P. Spirtes, C. Glymour, R. Scheines, 2007 .

Searching the DCM Model Space, and Some Generalizations

Clark Glymour, C. Glymour, Michael Freenor, 2010 .

Review of Thinking about Android Epistemology

C. Glymour, P. Hayes, K. Ford, 2006 .

Expert statistical testimony and epidemiological evidence: the toxic effects of lead exposure on children

C. Glymour, R. Scheines, S. Fienberg, 2003 .

Analysis of Microarray Data for Treated Fat Cells

P. Spirtes, C. Glymour, R. Scheines, 2003 .

Evidence of cross-hybridization artifact in expressed sequence tags (ESTs) on cDNA microarrays ⁄

P. Spirtes, C. Glymour, R. Scheines, 2002 .

Truth is among the best explanations: Finding causal explanations of conditional independence and dependence

Richard Scheines, Christopher Meek, Clark Glymour, 1999 .

Creative Abduction, Factor Analysis, and the Causes of Liberal Democracy

C. Glymour, 2019, KRITERION – Journal of Philosophy.

ING FOR CONTROL WITH STRUCTURAL CONSTRAINTS

C. Glymour, B. Schölkopf, Chaochao Lu, 2022 .

Action-Sufficient State Representation Learning for Control with Structural Constraints

José Miguel Hernández-Lobato, C. Glymour, B. Schölkopf, 2021, ICML.

Estimation of Voxelwise Effective Connectivities: Applications to High Connectivity Sub-Regions within Hippocampal and within Corticostriatal Networks

J. C. Liang, C. Glymour, Ruben Sanchez-Romero, 2016, bioRxiv.

Scale-Free Exponents of Resting State are Biomarkers of Neuro-Typical and Atypical Brain Activity

C. Glymour, S. Hanson, C. Hanson, 2016, bioRxiv.

TETRAD - A TOOLBOX FOR CAUSAL DISCOVERY

C. Glymour, E. Barnes, Kun Zhang, 2018 .

Effective Connectivity of Brain Networks Responsible for Impaired Joint Attention in Individuals with Autism

C. Glymour, S. Hanson, C. Hanson, 2012 .

A Processing Automaton for Intensive Data

James P. Crutchfield, Jeffrey D. Scargle, C. Glymour, 2001 .

To Save the Noumena

C. Glymour, 1976 .

Topology, cosmology and convention

C. Glymour, 1972, Synthese.

A Semantics and Methodology for Ceteris Paribus Hypotheses

C. Glymour, 2002 .

Foundations of Space-Time Theories: Minnesota Studies in the Philosophy of Science

C. Glymour, J. Earman, J. Stachel, 1977 .

[Bayesian Analysis in Expert Systems]: Comment: Conditional Independence and Causal Inference

P. Spirtes, C. Glymour, 1993 .

TETRAD II: Tools for Discovery

Richard Scheines, Christopher Meek, Clark Glymour, 1994 .

Thoroughly Modern Meno

Kevin T. Kelly, Clark Glymour, C. Glymour, 1988 .

Why you'll never know whether Roger Penrose is a computer

Kevin T. Kelly, Clark Glymour, C. Glymour, 1990, Behavioral and Brain Sciences.

Examining Holistic Medicine

C. Glymour, D. Stalker, 1986 .

Conﬁrmation and Chaos *

C. Glymour, Maralee Harrell, 2002 .

A Mind Is a Terrible Thing to Waste

C. Glymour, 1999, Philosophy of Science.

Determinism, Ignorance, and Quantum Mechanics

C. Glymour, 1971 .

Philosophical essays on Freud: Freud, Kepler, and the clinical evidence

C. Glymour, 1982 .

On the Possibility of Inference to the Best Explanation

C. Glymour, 2012, J. Philos. Log..

Latent Hierarchical Causal Structure Discovery with Rank Constraints

C. Glymour, Biwei Huang, Feng Xie, 2022, NeurIPS.

Probability and the Explanatory Virtues

C. Glymour, 2015, The British Journal for the Philosophy of Science.

Default Reasoning and the Logic of Theory Perturbation

Richmond H. Thomason, Clark Glymour, C. Glymour, 1984, Non-Monotonic Reasoning.

The gravitational red shift as a test of general relativity: history and analysis.

C. Glymour, J. Earman, 1980 .

Einstein and Hilbert: Two months in the history of general relativity

C. Glymour, J. Earman, 1978 .

The enhanced warfighter

C. Glymour, K. Ford, 2014, Militarization.

Actual causation: a stone soup essay

Choh Man Teng, Frederick Eberhardt, Richard Scheines, 2009, Synthese.

Reply to comment

P. Spirtes, C. Glymour, Christopher Meek, 1995 .

Inferring causation from time series in Earth system sciences

Bernhard Schölkopf, Marten Scheffer, Markus Reichstein, 2019, Nature Communications.

Causal Discovery

C. Glymour, 2010, Encyclopedia of Machine Learning.

The TETRAD Project: Constraint Based Aids to Causal Model Specification.

R Scheines, P Spirtes, C Glymour, 1998, Multivariate behavioral research.

Hans Reichenbach's Probability Logic

Frederick Eberhardt, Clark Glymour, C. Glymour, 2011, Inductive Logic.

Why Probability Does Not Capture the Logic of Scientific Justification

Kevin T. Kelly, Clark Glymour, C. Glymour, 2004 .

Lost in the tensors: Einstein's struggles with covariance principles 1912–1916

C. Glymour, J. Earman, 1978 .

Estimating feedforward and feedback effective connections from fMRI time series: Assessments of statistical methods

Clark Glymour, Ruben Sanchez-Romero, Biwei Huang, 2019, Network Neuroscience.

The center for causal discovery of biomedical knowledge from big data

Richard Scheines, Gregory F. Cooper, Clark Glymour, 2015, J. Am. Medical Informatics Assoc..

Non-Gaussian methods and high-pass filters in the estimation of effective connections

Clark Glymour, Ruben Sanchez-Romero, Joseph Ramsey, 2014, NeuroImage.

Diagnosis of Autism Spectrum Disorder by Causal Influence Strength Learned from Resting-State fMRI Data pdf

Clark Glymour, Kun Zhang, Joseph Ramsey, 2019, ArXiv.

Causal Discovery from Multiple Data Sets with Non-Identical Variable Sets

Kun Zhang, Clark Glymour, Mingming Gong, 2020, AAAI.

Comorbid science? 1

Richard Scheines, David Danks, Clark Glymour, 2010, Behavioral and Brain Sciences.

Six problems for causal inference from fMRI

Russell A. Poldrack, Clark Glymour, Yaroslav O. Halchenko, 2010, NeuroImage.

N-1 Experiments Suffice to Determine the Causal Relations Among N Variables

Frederick Eberhardt, Richard Scheines, Clark Glymour, 2006 .

On the Number of Experiments Sufficient and in the Worst Case Necessary to Identify All Causal Relations Among N Variables pdf

Frederick Eberhardt, Richard Scheines, Clark Glymour, 2005, UAI.

Domain Adaptation As a Problem of Inference on Graphical Models pdf

Kun Zhang, Petar Stojanov, Clark Glymour, 2020, NeurIPS.

Causal Discovery from Heterogeneous/Nonstationary Data pdf

Bernhard Schölkopf, Kun Zhang, Clark Glymour, 2019, J. Mach. Learn. Res..

Causal Discovery from Nonstationary/Heterogeneous Data: Skeleton Estimation and Orientation Determination

Bernhard Schölkopf, Clark Glymour, Kun Zhang, 2017, IJCAI.

On meta-analyses of imaging data and the mixture of records

Clark Glymour, Joseph Ramsey, Peter Spirtes, 2011, NeuroImage.

Statistical Themes and Lessons for Data Mining

Padhraic Smyth, Clark Glymour, David Madigan, 2004, Data Mining and Knowledge Discovery.

Mixed graphical models for integrative causal analysis with application to chronic lung disease diagnosis and prognosis

Clark Glymour, Dimitris V. Manatakis, Joseph Ramsey, 2018, Bioinform..

Problems for Structure Learning Aggregation and Computational Complexity

David Danks, Clark Glymour, Tianjiao Chu, 2009 .

Comparison of strategies for scalable causal discovery of latent variable models from mixed data

Panos K. Chrysanthis, Dimitrios V. Manatakis, P. Spirtes, 2018, International Journal of Data Science and Analytics.

An evaluation of machine-learning methods for predicting pneumonia mortality

Constantin F. Aliferis, Tom M. Mitchell, Rich Caruana, 1997, Artif. Intell. Medicine.

Evidence of systematic expressed sequence tag IMAGE clone cross-hybridization on cDNA microarrays.

Larry Wasserman, Richard Scheines, Clark Glymour, 2004, Genomics.

Reichenbach’s Entanglements

Clark Glymour, C. Glymour, 1977 .

Discovery and Visualization of Nonstationary Causal Models

Kun Zhang, Clark Glymour, Jiji Zhang, 2015, 1509.08056.

On Causal Discovery in the Presence of Changing Causal Models

Kun Zhang, Clark Glymour, Jiji Zhang, 2015 .

Atypical Effective Connectivity of Social Brain Networks in Individuals with Autism

Clark Glymour, Stephen Jose Hanson, Catherine Hanson, 2013, Brain Connect..

Watching Social Science

C. Glymour, M. Glymour, Bruce Glymour, 2008 .

The Debate About the Effects of Exposure to Televised Violence on Aggressive Behavior

C. Glymour, M. Glymour, Bruce Glymour, 2008 .

Search for Additive Nonlinear Time Series Causal Models

Clark Glymour, Tianjiao Chu, C. Glymour, 2008, J. Mach. Learn. Res..

Generalized Score Functions for Causal Discovery

Bernhard Schölkopf, Clark Glymour, Kun Zhang, 2018, KDD.

Engineers, cranks, physicians, magicians.

C. Glymour, D. Stalker, 1983, The New England journal of medicine.

Identification of Mechanisms of Functional Signaling Between Human Hippocampus Regions

Clark Glymour, Ruben Sanchez-Romero, Joseph Ramsey, 2017, bioRxiv.

Inductive inference from theory laden data

Kevin T. Kelly, Clark Glymour, C. Glymour, 1992, J. Philos. Log..

Hypothetico-Deductivism Is Hopeless

Clark Glymour, C. Glymour, 1980, Philosophy of Science.

Books Received

C. Glymour, P. Hayes, E. Feigenbaum, 1994, The Journal of the American Board of Family Medicine.

Inferring causation from time series in Earth system sciences

P. Spirtes, C. Glymour, B. Schölkopf, 2019, Nature Communications.

Einstein and the History of General Relativity. Don Howard and John Stachel, Eds. Birkhauser Boston, Cambridge, MA, 1989. xii, 445 pp. $69. Einstein Studies, vol. 1. From a conference, North Andover, MA, May 1986.

C. Glymour, 1990, Science.

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

Clark Glymour, Joseph Ramsey, Madelyn Glymour, 2016, International Journal of Data Science and Analytics.

Multi-subject search correctly identifies causal connections and most causal directions in the DCM models of the Smith et al. simulation study

Clark Glymour, Joseph Ramsey, Stephen Jose Hanson, 2011, NeuroImage.

Behind Distribution Shift: Mining Driving Forces of Changes and Causal Arrows

Bernhard Schölkopf, Clark Glymour, Jiji Zhang, 2017, 2017 IEEE International Conference on Data Mining (ICDM).

Causal Discovery with Linear Non-Gaussian Models under Measurement Error: Structural Identifiability Results

Kun Zhang, Mingming Gong, Clark Glymour, 2018, UAI.

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

Richard Scheines, Kevin T. Kelly, Clark Glymour, 1987 .

Theoretical Realism and Theoretical Equivalence

Clark Glymour, C. Glymour, 1970, PSA: Proceedings of the Biennial Meeting of the Philosophy of Science Association.

Exploring Causal Structure with the TETRAD Program

Richard Scheines, Clark Glymour, Peter Spirtes, 1988 .

Automated Search for Causal Relations: Theory and Practice

Richard Scheines, Clark Glymour, Peter Spirtes, 2010 .

Causal modeling with the TETRAD program

Richard Scheines, Clark Glymour, C. Glymour, 1986, Synthese.

Causal inference

P. Spirtes, P. Spirtes, C. Glymour, 1991, Erkenntnis.

Artificial Intelligence for Statistical and Causal Modelling

Clark Glymour, C. Glymour, 1988 .

Development of Intelligent, Computerized Aids for the Specification of Casual Models from Large Data Bases

Richard Scheines, Clark Glymour, Peter Spirtes, 1992 .

A qualitative approach to causal modeling

Richard Scheines, Clark Glymour, Peter Spirtes, 1991 .

What Revisions Does Bootstrap Testing Need

Clark Glymour, John Earman, C. Glymour, 1988 .

What Revisions Does Bootstrap Testing Need? A Reply

C. Glymour, J. Earman, 1988, Philosophy of Science.

Causal Discovery of Feedback Networks with Functional Magnetic Resonance Imaging

Kun Zhang, Clark Glymour, Joseph Ramsey, 2018, bioRxiv.

Confirmation And Chaos*

Clark Glymour, Maralee Harrell, C. Glymour, 2002, Philosophy of Science.

Unconscious mental processes

C. Glymour, 1990, Behavioral and Brain Sciences.

Special Symposium on CHINESE TRADITIONAL MEDCINE

C. Glymour, Wang Wei, É. Micollier, 2010 .

Generalized Independent Noise Condition for Estimating Latent Variable Causal Graphs

C. Glymour, Kun Zhang, Biwei Huang, 2020, NeurIPS.

Causal Discovery and Forecasting in Nonstationary Environments with State-Space Models

Kun Zhang, Clark Glymour, Biwei Huang, 2019, ICML.

Triad Constraints for Learning Causal Structure of Latent Variables

Ruichu Cai, Clark Glymour, Feng Xie, 2019, NeurIPS.

Learning the Structure of Linear Latent Variable Models

Richard Scheines, Clark Glymour, Ricardo Bezerra de Andrade e Silva, 2006, J. Mach. Learn. Res..

Constructing Bayesian Network Models of Gene Expression Networks from Microarray Data

Richard Scheines, Clark Glymour, Stuart Kauffman, 2000 .

TETRAD II : tools for causal modeling

Richard Scheines, Christopher Meek, Clark Glymour, 1994 .

Logic, rational agency, and intelligent interaction

Dag Westerståhl, J.F.A.K. van Benthem, C. Glymour, 2009 .

On Some Patterns of Reduction

Clark Glymour, C. Glymour, 1970, Philosophy of Science.

Learning Measurement Models for Unobserved Variables

Richard Scheines, Clark Glymour, Ricardo Bezerra de Andrade e Silva, 2002, UAI.

Automated Remote Sensing with Near Infrared Reflectance Spectra: Carbonate Recognition

Clark Glymour, Joseph Ramsey, Peter Spirtes, 2002, Data Mining and Knowledge Discovery.

Learning Causes: Psychological Explanations of Causal Explanation1

Clark Glymour, C. Glymour, 1998, Minds and Machines.

Learning Causes: Psychological Explanations of Causal Explanation1

C. Glymour, 1998, Minds and Machines.

In Place of Regression

P. Spirtes, C. Glymour, R. Scheines, 1994 .

FRITL: A Hybrid Method for Causal Discovery in the Presence of Latent Confounders pdf

Ruichu Cai, Clark Glymour, Zhifeng Hao, 2021, ArXiv.

What Do College Ranking Data Tell Us About Student Retention

Marek J. Druzdzel, Clark Glymour, Marek J Druzdzel, 1994 .

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

C. Glymour, Ruben Sanchez-Romero, J. Ramsey, 2016, International Journal of Data Science and Analytics.

Causal Discovery from Heterogeneous/Nonstationary Data with Independent Changes.

Clark Glymour, Jiji Zhang, Bernhard Scholkopf, 2019 .

Clark Glymour’s responses to the contributions to the Synthese special issue “Causation, probability, and truth: the philosophy of Clark Glymour”

C. Glymour, 2016, Synthese.

Bayesian Ptolemaic Psychology

C. Glymour, 2006 .

Generalized Independent Noise Condition for Estimating Linear Non-Gaussian Latent Variable Graphs pdf

Ruichu Cai, Clark Glymour, Zhifeng Hao, 2020, ArXiv.

Tetrad II: User's Manual

Richard Scheines, Christopher Meek, Clark Glymour, 1994 .

Psychological and Normative Theories of Causal Power and the Probabilities of Causes

Clark Glymour, C. Glymour, 1998, UAI.

The expected complexity of problem solving

Kevin T. Kelly, Clark Glymour, Peter Spirtes, 1987 .

Poincaré’s Probabilities, Kantified, Post-Modernized

C. Glymour, 2014 .

Learning the Structure of Deterministic Systems

Clark Glymour, C. Glymour, 2007 .

The Evaluation of Discovery: Models, Simulation and Search through “Big Data”

Clark Glymour, Kun Zhang, Joseph Ramsey, 2019, Open Philosophy.

Causal Discovery in the Presence of Measurement Error: Identifiability Conditions pdf

Mingming Gong, Clark Glymour, Kun Zhang, 2017, ArXiv.

Why I am not a Bayesian

Clark Glymour, C. Glymour, 2016 .

Reverse Inference in Neuropsychology

Clark Glymour, Catherine Hanson, C. Glymour, 2016, The British Journal for the Philosophy of Science.

On the Methods of Cognitive Neuropsychology

Clark Glymour, C. Glymour, 1994, The British Journal for the Philosophy of Science.

Cognitive Orthoses: Toward Human-Centered AI

Kenneth M. Ford, Patrick J. Hayes, James F. Allen, 2015, AI Mag..

Making Things Happen: A Theory of Causal Explanation

C. Glymour, J. Woodward, 2004 .

Bootstraps and Probabilities

Clark Glymour, C. Glymour, 1980 .

Inductive inference in the limit

Clark Glymour, C. Glymour, 1985 .

The automation of discovery

Clark Glymour, C. Glymour, 2004, Daedalus.

Causal Discovery from Temporally Aggregated Time Series

Bernhard Schölkopf, Dacheng Tao, Mingming Gong, 2017, UAI.

Comparison of strategies for scalable causal discovery of latent variable models from mixed data

Panos K. Chrysanthis, Clark Glymour, Joseph Ramsey, 2018, International Journal of Data Science and Analytics.

Discovery Algorithms for Causally Sufficient Structures

Richard Scheines, Clark Glymour, Peter Spirtes, 1993 .

Driven Methods for Granger Causality and Contemporaneous Causality with Non-Linear Corrections : Climate Teleconnection Mechanisms

C. Glymour, D. Danks, T. Chu, 2005 .

Estimating feedforward and feedback effective connections from fMRI time series: Assessments of statistical methods

C. Glymour, Kun Zhang, Biwei Huang, 2019, Network Neuroscience.

Inferring causation from time series in Earth system sciences

P. Spirtes, C. Glymour, J. Peters, 2019, Nature Communications.

Domain Adaptation As a Problem of Inference on Graphical Models

C. Glymour, Mingming Gong, Kun Zhang, 2020, NeurIPS.

FINANCE AND GROWTH: THEORY AND EVIDENCE

C. Glymour, P. Horwich, 2005 .

Multi-subject search correctly identifies causal connections and most causal directions in the DCM models of the Smith et al. simulation study

C. Glymour, S. Hanson, J. Ramsey, 2011, NeuroImage.

Six problems for causal inference from fMRI

C. Glymour, S. Hanson, R. Poldrack, 2010, NeuroImage.

Discussion of Causal Diagrams for Empirical Research by J. Pearl

Stephen E. Fienberg, Clark Glymour, Peter Spirtes, 1995 .

Rabbit Hunting

Clark Glymour, C. Glymour, 2004, Synthese.

The TETRAD Project: Constraint Based Aids to Causal Model Specification.

P. Spirtes, C. Glymour, R. Scheines, 1998, Multivariate behavioral research.

Artificial Intelligence in Medicine

Geoffrey J. Gordon, Tom Michael Mitchell, P J Drew, 2018, European Archives of Medical Research.

Discovering How Brains Do Things

C. Glymour, S. Hanson, 2010 .

Artificial intelligence in medicine

Geoffrey J. Gordon, Johanna D. Moore, Tom Michael Mitchell, 1989, Springer US.

JON WILLIAMSON Bayesian Nets and Causality

Clark Glymour, C. Glymour, 2009, The British Journal for the Philosophy of Science.

Generalized Independent Noise Condition for Estimating Causal Structure with Latent Variables

C. Glymour, Biwei Huang, Zhen Chen, 2023, 2308.06718.

Classification and filtering of spectra: A case study in mineralogy

Clark Glymour, Ricardo Bezerra de Andrade e Silva, Joseph Vanderwaart, 2002, Intell. Data Anal..

Reliability, Realism, and Relativism

Kevin T. Kelly, C. Glymour, Cory Juhl, 1992 .

A Fast Algorithm for Discovering Sparse Causal Graphs

Clark Glymour, Peter Spirtes, P. Spirtes, 1991 .

Artificial intelligence in medicine

Diana E. Forsythe, Bruce G. Buchanan, Geoffrey J. Gordon, 1989, Springer US.

The prehistory of android epistemology

Kenneth M. Ford, Patrick J. Hayes, Clark Glymour, 1995 .