Richard Scheines

Publication Activity (10 Years)

Years Active: 1992-2018
Publications (10 Years): 2

Top Topics

Causal Discovery

Implicit Knowledge

Conditional Independence

Top Venues

J. Am. Medical Informatics Assoc.

Publications

Kenneth R. Koedinger, Richard Scheines, Peter Schaldenbrand
Is the Doer Effect Robust Across Multiple Data Sets? EDM (2018)
Richard Scheines, Joseph D. Ramsey
Measurement Error and Causal Discovery. CFA@UAI (2016)
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, Richard Scheines
The center for causal discovery of biomedical knowledge from big data. J. Am. Medical Informatics Assoc. 22 (6) (2015)
Richard Scheines, Elizabeth Silver, Ilya M. Goldin
Discovering Prerequisite Relationships Among Knowledge Components. EDM (2014)
Erich Kummerfeld, Joe Ramsey, Renjie Yang, Peter Spirtes, Richard Scheines
Causal Clustering for 2-Factor Measurement Models. ECML/PKDD (2) (2014)
Martina A. Rau, Richard Scheines, Vincent Aleven, Nikol Rummel
Does Representational Understanding Enhance Fluency - Or Vice Versa? Searching for Mediation Models. EDM (2013)
Tianjiao Chu, Richard Scheines, Peter Spirtes
Semi-Instrumental Variables: A Test for Instrument Admissibility CoRR (2013)
Ryan Carlson, Konstantin Genin, Martina A. Rau, Richard Scheines
Student Profiling from Tutoring System Log Data: When do Multiple Graphical Representations Matter? EDM (2013)
Frederick Eberhardt, Clark Glymour, Richard Scheines
On the Number of Experiments Sufficient and in the Worst Case Necessary to Identify All Causal Relations Among N Variables CoRR (2012)
Ricardo Bezerra de Andrade e Silva, Richard Scheines, Clark Glymour, Peter Spirtes
Learning Measurement Models for Unobserved Variables CoRR (2012)
Martina A. Rau, Richard Scheines
Searching for Variables and Models to Investigate Mediators of Learning from Multiple Representations. EDM (2012)
Patrik O. Hoyer, Aapo Hyvärinen, Richard Scheines, Peter Spirtes, Joseph D. Ramsey, Gustavo Lacerda, Shohei Shimizu
Causal discovery of linear acyclic models with arbitrary distributions CoRR (2012)
Matthew W. Easterday, Vincent Aleven, Richard Scheines, Sharon M. Carver
Using Tutors to Improve Educational Games. AIED (2011)
Frederick Eberhardt, Patrik O. Hoyer, Richard Scheines
Combining Experiments to Discover Linear Cyclic Models with Latent Variables. AISTATS (2010)
Clark Glymour, David Danks, Bruce Glymour, Frederick Eberhardt, Joseph D. Ramsey, Richard Scheines, Peter Spirtes, Choh Man Teng, Jiji Zhang
Actual causation: a stone soup essay. Synth. 175 (2) (2010)
Benjamin Shih, Kenneth R. Koedinger, Richard Scheines
Unsupervised Discovery of Student Strategies. EDM (2010)
Matthew W. Easterday, Vincent Aleven, Richard Scheines, Sharon M. Carver
Will Google destroy western democracy? Bias in policy problem solving. AIED (2009)
Matthew W. Easterday, Vincent Aleven, Richard Scheines, Sharon M. Carver
Constructing Causal Diagrams to Learn Deliberation. Int. J. Artif. Intell. Educ. 19 (4) (2009)
Patrik O. Hoyer, Aapo Hyvärinen, Richard Scheines, Peter Spirtes, Joseph D. Ramsey, Gustavo Lacerda, Shohei Shimizu
Causal discovery of linear acyclic models with arbitrary distributions. UAI (2008)
Benjamin Shih, Kenneth R. Koedinger, Richard Scheines
A Response Time Model For Bottom-Out Hints as Worked Examples. EDM (2008)
Benjamin Shih, Kenneth R. Koedinger, Richard Scheines
Optimizing Student Models for Causality. AIED (2007)
Matthew W. Easterday, Vincent Aleven, Richard Scheines
'Tis Better to Construct than to Receive? The Effects of Diagram Tools on Causal Reasoning. AIED (2007)
Ricardo Bezerra de Andrade e Silva, Richard Scheines, Clark Glymour, Peter Spirtes
Learning the Structure of Linear Latent Variable Models. J. Mach. Learn. Res. 7 (2006)
Ricardo Bezerra de Andrade e Silva, Richard Scheines
Bayesian learning of measurement and structural models. ICML (2006)
Ricardo Bezerra de Andrade e Silva, Richard Scheines
Towards Association Rules with Hidden Variables. PKDD (2006)
Frederick Eberhardt, Clark Glymour, Richard Scheines
On the Number of Experiments Sufficient and in the Worst Case Necessary to Identify All Causal Relations Among N Variables. UAI (2005)
Ricardo Bezerra de Andrade e Silva, Richard Scheines
New d-separation identification results for learning continuous latent variable models. ICML (2005)
Ricardo Bezerra de Andrade e Silva, Richard Scheines, Clark Glymour, Peter Spirtes
Learning Measurement Models for Unobserved Variables. UAI (2003)
Tianjiao Chu, Clark Glymour, Richard Scheines, Peter Spirtes
A Statistical Problem for Inference to Regulatory Structure from Associations of Gene Expression Measurements with Microarrays. Bioinform. 19 (9) (2003)
Richard Scheines, Gregory F. Cooper, Changwon Yoo, Tianjiao Chu
Piecewise Linear Instrumental Variable Estimation of Causal Influence. AISTATS (2001)
Tianjiao Chu, Richard Scheines, Peter Spirtes
Semi-Instrumental Variables: A Test for Instrument Admissibility. UAI (2001)
Richard Scheines
Estimating Latent Causal Inferences: Tetrad II model selection and Bayesian parameter estimation. AISTATS (1997)
Richard Scheines, Wilfried Sieg
Computer Environments for Proof Construction. Interact. Learn. Environ. 4 (2) (1994)
Richard Scheines, Peter Spirtes
Finding latent variable models in large databases. Int. J. Intell. Syst. 7 (7) (1992)