Faculty

Institution

Unit

Venue

Richard Scheines

Professor

Carnegie Mellon University

Affiliations

Dietrich College of Humanities and Social Sciences

History

Philosophy

School of Computer Science

Machine Learning

Richard Scheines is currently serving as a Dietrich College Of Humanities And Social Sciences Professor at Carnegie Mellon University.

Honors

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Experiences

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Education

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Richard Scheines

Carnegie Mellon University

Professor

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statistics causal inference causality causal reasoning inference data science education causal models knowledge discovery computer science machine learning philosophy of science automated reasoning computing education educational research statistical inference educational data mining artificial intelligence educational assessment decision science latent variable models computational intelligence behavioral research microarray data analysis educational psychology social science research machine learning research applied philosophy predictability higher education human decision process behavioral decision making intellectual discourse learning theory sociological research online learning ai education structure discovery causal structure genome biology regression analysis applied cognitive psychology teaching methods student learning psychometrics causal relations causal discovery tetrad project structural equation modeling structural equation models decision analysis curriculum & instruction experimental analysis constraint solving constraint programming computational learning theory tetrad program structural coefficient bias microarray data estimation theory intervention mechanism evidence-based interventions constraint satisfaction supervised learning individual decision making organizational psychology social work research self-efficacy theory mindset bayesian estimation model specification gene expression networks bayesian network models multidimensional data bottom-out hints worked examples intelligent systems history of philosophy descriptive statistics history of mathematics preservice teacher education history and philosophy of science phenomenology (philosophy) applied artificial intelligence teacher education historical review intelligent computing professional development education research statistical thinking history of science educational strategies practical philosophy soft computing philosophy of history educational contexts applied statistics image analysis optical image recognition statistical hypothesis test philosophical inquiry interdisciplinary history statistical theory statistical software behavioral statistic digital image processing

Video Presentations

Notable Scholarly Works

Notable Federal Grants

Collaborators

Prediction. Springer eBooks (2020)

Virgil Zeigler-HillTodd K. Shackelford

Causal Discovery With Tetrad in Learnsphere's Tigris. Edm 2019 Proceedings of the 12th International Conference on Educational Data Mining (2019)

inferencelearning sciencesdata scienceeducational assessmentinformaticscausal inferenceknowledge discoverydata miningdata modelingcausal relation extractionstatisticseducational strategiescausal modelslearning analyticscausal reasoningeducational data miningscientific dataeducationdata-driven learningstatistical softwaremachine learning researchanalytics

Is the Doer Effect Robust Across Multiple Data Sets?. Proceedings of the 11th International Conference on Educational Data Mining Edm 2018 (2018)

data setsdata scienceeducational assessmentprogram evaluationknowledge discoverydata modelingdata miningdata-driven sciencestatisticslearning analyticshigher educationeducational data miningscientific dataapplied statisticsstatistical hypothesis testeducationlarge-scale datasetshigher education assessmentbehavioral statisticstatistical softwaremissing dataanalytics

Is the Doer Effect Robust Across Multiple Data Sets. Educational Data Mining (2018)

Kenneth R. KoedingerRichard ScheinesPeter Schaldenbrand

educationdoer effectmultiple data setseducational researchdata miningeducational data miningmachine learning

The Limits of Causal Knowledge. Research Showcase @ Carnegie Mellon University (Carnegie Mellon University) (2018)

James M. RobinsRichard ScheinesPeter SpirtesLarry Wasserman

philosophycausal knowledgeepistemologycausality

Time and Attention: Students, Sessions, and Tasks. Research Showcase @ Carnegie Mellon University (Carnegie Mellon University) (2018)

Andrew ArnoldRichard ScheinesJoseph E. BackBill Jerome

educational psychologyeducationattentiontime perceptiontask performancestudent learningtask analysispsychology

Finding Pure Submodels for Improved Differentiation of Bifactor and Second-Order Models. STRUCTURAL EQUATION MODELING-A MULTIDISCIPLINARY JOURNAL (2017)

functional data analysissimultaneous equation modelinglongitudinal studiesbusiness researchmathematical biosciencemodeling methodstructural problemmodel selectiondevelopmental dynamicnonlinear analysislatent variable modelsmodel analysismodeling and simulationeconomic modellingbehavioral sciencesstatistical modelsbusinesssystems modelinggeneral systemsmultiscale modelingquantitative science studies

Finding Pure Submodels for Improved Differentiation of Bifactor and Second-Order Models. Structural Equation Modeling A Multidisciplinary Journal (2017)

Renjie YangPeter SpirtesRichard ScheinesSteven P. ReiseMaxwell Mansoff

pure submodelsapplied mathematicsmachine learningsecond-order models

Measurement Error and Causal Discovery. (2017)

statistical hypothesis testcausal inferencestatistical methodologycausalityinferencecausal reasoningquality engineeringdata sciencestatistics

Using Tutors to Improve Educational Games: A Cognitive Game for Policy Argument. JOURNAL OF THE LEARNING SCIENCES (2017)

behavioral educationgame designlearning-by-doinglearning sciencespreservice teacher educationperformance studiesgamificationeducational technologyinteractive learningeducational psychologycognitive scienceeducational strategiesinstructional designeducational researchcomputing educationlearning analyticseducational gamesmathematics educationperformative teachingchild-computer interactioneducationexperiential learning

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Richard Scheines

Professor

Carnegie Mellon University

Affiliations

Dietrich College of Humanities and Social Sciences

History

Philosophy

School of Computer Science

Machine Learning

Richard Scheines is currently serving as a Dietrich College Of Humanities And Social Sciences Professor at Carnegie Mellon University.

Honors

Create free account to unlock more features

Experiences

Create free account to unlock more features

Education

Create free account to unlock more features

Richard Scheines

Carnegie Mellon University

Professor

You can click a keyword to filter the displayed items

Video Presentations

Notable Scholarly Works

Notable Federal Grants

Collaborators

Prediction. Springer eBooks (2020)

Virgil Zeigler-HillTodd K. Shackelford

Causal Discovery With Tetrad in Learnsphere's Tigris. Edm 2019 Proceedings of the 12th International Conference on Educational Data Mining (2019)

Is the Doer Effect Robust Across Multiple Data Sets?. Proceedings of the 11th International Conference on Educational Data Mining Edm 2018 (2018)

Is the Doer Effect Robust Across Multiple Data Sets. Educational Data Mining (2018)

Kenneth R. KoedingerRichard ScheinesPeter Schaldenbrand

educationdoer effectmultiple data setseducational researchdata miningeducational data miningmachine learning

The Limits of Causal Knowledge. Research Showcase @ Carnegie Mellon University (Carnegie Mellon University) (2018)

James M. RobinsRichard ScheinesPeter SpirtesLarry Wasserman

philosophycausal knowledgeepistemologycausality

Time and Attention: Students, Sessions, and Tasks. Research Showcase @ Carnegie Mellon University (Carnegie Mellon University) (2018)

Andrew ArnoldRichard ScheinesJoseph E. BackBill Jerome

educational psychologyeducationattentiontime perceptiontask performancestudent learningtask analysispsychology

Finding Pure Submodels for Improved Differentiation of Bifactor and Second-Order Models. STRUCTURAL EQUATION MODELING-A MULTIDISCIPLINARY JOURNAL (2017)

Finding Pure Submodels for Improved Differentiation of Bifactor and Second-Order Models. Structural Equation Modeling A Multidisciplinary Journal (2017)

Renjie YangPeter SpirtesRichard ScheinesSteven P. ReiseMaxwell Mansoff

pure submodelsapplied mathematicsmachine learningsecond-order models

Measurement Error and Causal Discovery. (2017)

statistical hypothesis testcausal inferencestatistical methodologycausalityinferencecausal reasoningquality engineeringdata sciencestatistics

Using Tutors to Improve Educational Games: A Cognitive Game for Policy Argument. JOURNAL OF THE LEARNING SCIENCES (2017)

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5
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10 / page

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