Dean, Dietrich College of Humanities & Social Sciences
Professor of Philosophy, Machine Learning, and HCII
My research is on causal discovery, in particular the problem of learning about causation from statistical evidence. The theoretical and computational dimensions of this work have come to be called the TETRADproject, which represents nearly 25 years of collaboration with Clark Glymour, Peter Spirtes and many, many others. Building efficient and practically useful algorithms for causal discovery is as much computer science as philosophy, and thus I have a courtesy appointment in the Machine Learning Department.
I have also put a lot of effort into building and researching the effectiveness of educational software, ranging from intelligent proof tutors to virtual causality labs to a full semester course on Causal and Statistical Reasoning. Because of this work I have a courtesy appointment in the Human-Computer Interaction Institute.
Ph.D. - 1987: History and Philosophy of Science, University of Pittsburgh
- Thesis: Causal Models in the Social Sciences.
- Advisor: Clark Glymour
- National Research Council. Review of EPA's Integrated Risk Information System (IRIS) Process (2014). The National Academies Press, Washington, DC.
- Improving the Presumptive Disability Decision-Making Process (2007). Committee on Evaluation of the Presumptive Disability Decision-Making Process for Veterans, Institute of Medicine of the National Academy of Science, J. Samet, and C. Bodurow, editors. The National Academies Press, Washington, D.C.
- Food Marketing to Children and Youth: Threat or Opportunity, (2006).Committee on Food Marketing and the Diets of Children and Youth, Institute of Medicine of the National Academy of Science, The National Academies Press, Washington, D.C.
- Causation, Prediction, and Search (2000), 2nd edition, P. Spirtes, C. Glymour, and R. Scheines, MIT Press, Boston
- Discovering Causal Structure: Artificial Intelligence, Philosophy of Science, and Statistical Modeling(1987), C. Glymour, R. Scheines, P. Spirtes, and K. Kelly. Academic Press, San Diego, CA.
- TETRAD II: Tools for Causal Discovery (1994), R. Scheines, P. Spirtes, C. Glymour, and C. Meek, Lawrence Erlbaum Associates, Hillsdale, N.J.
- Bonifay, W., S. Reise, R. Scheines, R. Meijer (in press). "When are multidimensional data unidimensional enough for structural equation modeling? An evaluation of the DETECT multidimensional index," Structural Equation Modeling.
- Wheeler, G., and Scheines, R. (2013). Coherence and Confirmation Through Causation, Mind, 122 (485), 135-170.
- Reise, S., Scheines, R., Widaman, K., and Haviland, M. (2013). Multidimensionality and Structural Coefficient Bias in Structural Equation Modeling: A Bifactor Perspective, Educational and Psychological Measurement, V. 73, Issue 1.
- Eberhardt, F., Hoyer, P.O., & Scheines, R. (2010). Combining Experiments to Discover Linear Cyclic Models with Latent Variables. In Journal of Machine Learning, Workshop and Conference Proceedings (AISTATS 2010), 9:185-192.
- Glymour, C., Danks, D., Glymour, B., Eberhardt, F., Ramsey, J., Scheines, R. (2010). Actual Causation: a stone soup essay., Synthese, Volume 175, Issue 2 Page 169-192.
- Wheeler, G., and Scheines, R. (2010). Causation, Association, and Confirmation. in Explanation, Prediction, and Confirmation. New Trends and Old Ones Reconsidered, edited by Stephan Hartmann, Marcel Weber, Wenceslao, J. Gonzalez, Dennis Dieks, Thomas Uebe, Springer.
- Livengood, J., Sytsma, Feltz, A., Scheines, R., and Machery, E (2010). Philosophical Temperament, chapter 1.3 in Philosophical Psychology, 23: 3, 313-330.
- Scheines, R. (2008), Causation, Truth, and the Law , Brooklyn Law Review, 73, 2
- Eberhardt, F., and Scheines R., (2007),Interventions and Causal Inference, in PSA-2006, Proceedings of the 20th biennial meeting of the Philosophy of Science Association 2006 http://philsci.org/news/PSA06
- Silva, R., Glymour, C., Scheines, R. and Spirtes, P. (2006) , Learning the Structure of Latent Linear Structure Models, Journal of Machine Learning Research, 7, 191-246.
- Scheines, R. (2006). The Similarity of Causal Inference in Experimental and Non-Experimental Studies, Proceedings of the 2004 Biennial Meetings, Philosophy of Science, V. 72, N. 5, pp. 927-940.
- N-1 Experiments Suffice to Determine the Causal Relations Among N Variables, (2006).Eberhardt, F., Glymour, C., & Scheines, R. in Innovations in Machine Learning, Holmes, Dawn E.; Jain, Lakhmi C. (Eds.), Theory and Applications
Series: Studies in Fuzziness and Soft Computing, Vol. 194, Springer-Verlag.
- On the Number of Experiments Sufficient and in the Worst Case Necessary to Identify All Causal Relations Among N Variables, (2005). Eberhardt, F., Glymour, C., & Scheines, R. in Proceedings of the 21 st Conference on Uncertainty and Artificial Intelligence, Fahiem Bacchus and Tommi Jaakkola (editors), AUAI Press, Corvallis, Oregon, pp. 178-184.
- Causal Inference of Ambiguous Manipulations (2004). Spirtes, P, & Scheines, R.. Proceedings of the 2002 Philosophy of Science Association Meetings.
- Learning Measurement Models for Unobserved Variables, (2003). Silva, R., Scheines, R., Glymour, C., and Spirtes. P., in Proceedings of the Nineteenth Conference on Uncertainty in Artificial Intelligence , U. Kjaerulff and C. Meek, eds., Morgan Kauffman.
- Uniform Consistency in Causal Inference (2003) Robins, J., Scheines, R., Spirtes, P., and Wasserman, L., Biometrika, September, 90: 491-515. gs.
- Semi-Instrumental Variables: A Test for Instrument Admissibility, (2001). In Proceedings of the 17th Conference on Uncertainty in Artificial Intelligence, Univ. of Washington, Seattle, 20
- Piece-wise Linear Instrumental Variable Estimation of Causal Influence, (2001), Scheines, R., Cooper, G., Changwon Yoo,Tianjiao Chu, in Proceedings of Eighth International Workshop on Artificial Intelligence and Statistics, Morgan Kauffman.
- Constructing Bayesian Network Models of Gene Expression Networks from Microarray Data, (2001) Spirtes, P., Glymour, C., and Scheines, R. Kauffman, S.,Aimale, V., & Wimberly, F. in Proceedings of the Atlantic Symposium on Computational Biology, Genome Information Systems and Technology, Duke University, March.
- Bayesian Estimation and Testing of Structural Equation Models (1999). Scheines, R., Hoijtink, H., & Boomsma, A., Psychometrika. 64, 1, pp. 37-52.
Causation and Statistics - Reviews/Handbook/Encyclopedia Articles
- Spirtes, P., Glymour, C., Scheines, R., Tillman R. (2010). Automated Search for Causal Relations: Theory and Practice. In Heuristics, Probability, and Causality: A Tribute to Judea Pearl, edited by Rina Dechter, Hewctor Geffner, and Joseph Halpern, College Publications, 467-506.
- Causation, Statistics, and the Law, Scheines, R. (2008), Journal of Law and Policy, 16,
- Causation, (2004). Scheines, R., in New Dictionary of the History of Ideas . Charles Scribner & Sons.
- Causal Inference (2004). Spirtes, P., Scheines, R.,Glymour, C., Richardson, T., and Meek, C., in Handbook of Quantitative Methodology in the Social Sciences , ed. David Kaplan, Sage Publications, 447-478.
- Computation and Causation, (2002) R. Scheines, in MetaPhilosophy, 33: 1, Blackwell.
- Using Path Diagrams as a Structural Equation Modeling Tool, (1998). Spirtes, P., Richardson, T., Meek, C., Scheines, R., and Glymour, C., Sociological Methods & Research, Vol. 27, N. 2, 182-225.
- The TETRAD Project : Constraint Based Aids to Model Specification. (1998). Scheines, R., Spirtes, P., Glymour, C., Richardson, T., & Meek, C., Multivariate Behavioral Research, Vol. 33, N. 1, 65-118, & "Reply to Commentary," same issue, 165-180.
- An Introduction to Causal Inference, (1997), R. Scheines, in Causality in Crisis?, V. McKim and S. Turner (eds.), Univ. of Notre Dame Press, pp. 185-200.
Causation & Statistics - Applications
- Cryder, C., Loewenstein, G., Scheines, R. (2013). The Donor is in the Details, Organizational Behavior and Human Decision Processes, 120 (1), 15-23.
- Estimating Latent Causal Influences: TETRAD III Variable Selection and Bayesian Parameter Estimation: the effect of Lead on IQ, (2000). Scheines, R. Handbook of Data Mining, Pat Hayes, editor, Oxford University Press.
- A Statistical Problem for Inference to Regulatory Structure from Associations of Gene Expression Measurement with Microarrays (2002) Chu, Tianjaio, Glymour C., Scheines, R., & Spirtes, P. (to appear in Bioinformatics).
- Expert Statistical Testimony and Epidemiological Evidence: The Toxic Effects of Lead Exposure on Children(2002). Fienberg, S., Glymour, C., and Scheines, R.(2003). Journal of Econometrics, 113, 1, March, 33-48.
- Single Mothers' Self-Efficacy, Parenting in the Home Environment, and Children's Development in a Two-Wave Study, (2005). Jackson, A., and Scheines, R. in Social Work Research , 29, 1, pp. 7-20.
Education / Educational Computing / Educational Data Mining
- Rau, M., Scheines, R., Aleven, V., and Rummel, N. (2013). Does Representational Understanding Enhance Fluencey – or Vice Versa? Searching for Mediation Models. Proceedings of the 6th International Conference on Educational Data Mining (EDM 2013). Best-Paper Award
- Carlson, R., Genin, K., Rau, M., and Scheines, R., (2013). Student Profiling from Tutoring System Log Data: When do Multiple Graphical Representations Matter. Proceedings of the 6th International Conference on Educational Data Mining (EDM 2013)
- Rau, M., and Scheines, R. (2012). Searching for Variables and Models to Investigate Mediators of Learning from Multiple Representations, in Proceedings of the 5th International Conference on Educational Data Mining (EDM 2012)
- Easterday, M. W., Aleven, V., Scheines, R. & Carver, S. M. (2010). Constructing causal diagrams to learn deliberation. International Journal of Artificial Intelligence in Education.
- Shih, B., Kenneth R. Koedinger, and Richard Scheines. (2010). Discovery of Learning Tactics using Hidden Markov Model Clustering. in Proceedings of the 3rd International Conference on Educational Data Mining.
- Shih, B., Kenneth R. Koedinger, and Richard Scheines. A Response Time Model For Bottom-Out Hints as Worked Examples. In Proceedings of the 1st International Conference on Educational Data Mining, 2008, p. 117-126. Best Paper Award.
- Shih, B., Kenneth R. Koedinger, and Richard Scheines. Optimizing Student Models for Causality. In Proceedings of the 13th International Conference on Artificial Intelligence in Education 2007, p. 644-646.
- Teaching the Normative Theory of Causal Reasoning (2007). Scheines, R., Easterday, M., and Danks, D. in Alison Gopnik and Laura Schultz, editors, Oxford University Press.
- Easterday, M.W., Aleven, V., & Scheines, R. (2007). 'Tis better to construct than receive: The effects of diagramming tools on learning to analyze social policy. Proceedings of 13th International Conference on Artificial Intelligence in Education (AIED- 2007)
- Time and Attention: Students and Tasks (2005). Arnold , A., Scheines, R., Beck, J., and Jerome, B. AAAI-05: Educational Data Mining, Technical Report WS-05-02 AAAI Press.
- Replacing Lecture with Web-Based Course Materials (2005). Scheines, R., Leinhardt, G., Smith, J., and Cho, K. Journal of Eduational Computing Research, 32, 1, 1-26.
- Computer Environments for Proof Construction," (1994), R. Scheines and W. Sieg, in Interactive Learning Environments, Elliot Soloway (editor), Vol. 4, Issue (2), 159-169.