Books

• 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.

Articles

• Glymour, C., and R. Scheines, (1986). “Causal Modeling with the TETRAD Program,” Synthese, Vol. 68, No. 1, July, pp. 37-64.

Books

• Causation, Prediction, and Search (1993), P. Spirtes, C. Glymour, and R. Scheines, vol. 81 in Springer-Verlag's Lecture Notes in Statistics Series.
• TETRAD II: Tools for Causal Discovery (1994), R. Scheines, P. Spirtes, C. Glymour, and C. Meek, Lawrence Erlbaum Associates, Hillsdale, N.J.

Articles

• Glymour, C., R. Scheines, and P. Spirtes, (1988). Exploring Causal Structure with the TETRAD Program,” Sociological Methodology, Clifford Clogg (editor), American Sociological Association, Vol. 18, pp. 411-448.
• Spirtes, P., R. Scheines, and C. Glymour, (1990). Simulation Studies of the Reliability of Computer Aided Model Specification using the TETRAD, EQS, and LISREL Programs,” Sociological Methods and Research, Vol. 19, No. 1, pp. 3-66.
• Glymour, C., Spirtes, P. and Scheines, R. (1990). Independence Relations Produced by Parameter Values in Causal Models,” Philosophical Topics, Vol. 18, No. 2, pp. 55-70
• Spirtes, P., C. Glymour, and R. Scheines, (1991). “From Probability to Causality,” in Philosophical Studies, Vol. 64. No 1. pp. 1-36
• Glymour, C., P. Spirtes, and R. Scheines, (1991). “Causal Inference,” in Erkenntnis, Kluwer Academic Publishers, Vol. 35, Nos. 1-3., July, pp. 151-189
• Spirtes, P. and C. Glymour (1991). An Algorithm for Fast Recovery of Sparse Causal Graphs. Social Science Computer Review, Vol. 9, pp. 62-72.
• Scheines, R. and P. Spirtes,  (1992) Finding Latent Variable Models in Large Data Bases," in the International Journal of Intelligent Systems, edited by G. Piatetski-Shapiro, Vol. 7, No. 7, September, pp. 609-622.
• Glymour, C., P. Spirtes, and R. Scheines (1993). Inferring Causal Structure in Mixed Populations, in Artificial Intelligence Frontiers in Statistics: AI and Statistics III, D.J. Hand (editor), Chapman & Hall, London, pp. 141-155.
• C. Meek and R. Scheines, (1993). Causal Structure, Neural Networks, and Classification, in Conference Proceedings: Applications of Artificial Neural Networks and Related Technologies to Manpower, Personnel and Training, NPRDC-AP-93-10, Navy Personnel Research and Development Center, San Diego, CA, pp. 115-124.
• Glymour, C., P. Spirtes, and R. Scheines, (1994). In Place of Regression, in Patrick Suppes: Scientific Philosopher, Paul Humphreys (editor), Vol. 1, Kluwer Academic Publishers, Dordrecht, Holland.

Articles

• Scheines, R. (1996). Estimating Latent Causal Influences,” in Proceedings of the 6th International Workshop on Artificial Intelligence and Statistics, eds. P. Smythe and D. Madigan.
• Scheines,  R. (1997). An Introduction to Causal Inference, in Causality in Crisis?, V. McKim and S. Turner (eds.), Univ. of Notre Dame Press, pp. 185-200.
• Scheines, R., Spirtes, P., Glymour, C., Richardson, T., & Meek, C. (1998). “The TETRAD Project : Constraint Based Aids to Model Specification." Multivariate Behavioral Research, Vol. 33, N. 1, 65-118, & "Reply to Commentary," same issue, 165-180.
• Spirtes, P., Richardson, T., Meek, C., Scheines, R., and Glymour, C.,  (1998). “Using Path Diagrams as a Structural Equation Modeling Tool,” Sociological Methods & Research, Vol. 27, N. 2, 182-225.
• Spirtes, P., Glymour, C., Scheines, R., Meek, C., Feinberg, S., and Slate, E. (1999). Prediction and Experimental Design with Graphical Causal Models, in Computation and Causation, edited by C. Glymour and G. Cooper, MIT Press, Cambridge, MA, pp.  65-94.
• Scheines, R., Hoijtink, H., & Boomsma, A. (1999), Bayesian Estimation and Testing of Structural Equation Models, Psychometrika. 64, 1, pp. 37-52.

Books

• Causation, Prediction, and Search (2000), 2nd edition, P. Spirtes, C. Glymour, and R. Scheines, MIT Press, Boston.

Articles

• Spirtes, P., Glymour, C., Scheines, R., Kauffman, S., Aimalie, and Wimberly, F. (2001). Constructing Bayesian Network Models of Gene Expression Networks from Microarray Data," in Proceedings of the Atlantic Symposium on Computational Biology, Genome Information Systems and Technology, Duke University, March. 
• Chu, T., Scheines, R., and Spirtes, P. (2001). “Semi-Instrumental Variables: A Test for Instrument Admissibility, in Proceedings of the 17th Conference on Uncertainty in Artificial Intelligence, Univ. of Washington, Seattle, 20.
• Scheines, R., (2002), Estimating Latent Causal Influences: TETRAD III Variables Selection and Bayesian Parameter Estimation: Lead and IQ” Handbook of Data Mining and Knowledge Discovery, Pat Hayes, editor, Oxford University Press, 944-952.
• Chu, T., Glymour, C., Scheines, R., and Spirtes, P (2003). “A Statistical Problem for Inference to Regulatory Structure from Associations of Gene Expression Measurements with Microarrays,” Bioinformatics, 19: 1147-1152
• Feinberg, S., C. Glymour, and R. Scheines (2003). Expert Statistical Testimony and Epidemiological Evidence: The Toxic Effects of Lead Exposure on Children, Journal of Econometrics, Volume 113, Issue 1, March, 33-48.
• Spirtes, P., and Scheines, R. (2004).  Causal Inference of Ambiguous Manipulations, Philosophy of Science 71 (5):833-845 (2004).

Articles

• Robins, J., Scheines, R., Spirtes, P., and Wasserman, L. (2003). Uniform Consistency in Causal Inference, Biometrika, September,  90: 491 – 515.
• Spirtes, P., Scheines, R., Glymour, C., Richardson, T., and Meek, C. (2004), “Causal Inference,” in Handbook of Quantitative Methodology in the Social Sciences, ed. David Kaplan, Sage Publications, 447-478.
• Jackson, A., and Scheines, R. (2005). “Single Mothers’ Self-Efficacy, Parenting in the Home Environment, and Children’s Development in a Two-Wave Study” in Social Work Research, 29, 1, pp. 7-20.
• Eberhardt, F., Glymour, C., & Scheines, R. (2005), On the Number of Experiments Sufficient and in the Worst Case Necessary to Identify All Causal Relations Among N Variables, Proceedings of the 21st Conference on Uncertainty and Artificial Intelligence, Fahiem Bacchus and Tommi Jaakkola (editors), AUAI Press, Corvallis, Oregon, pp. 178-184. 

Articles

• Silva, R., Glymour, C., Scheines, R. and Spirtes, P. (2006) “Learning the Structure of Linear Latent 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.
• 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
• Scheines, R. (2008). “Causation, Statistics, and the Law”, Journal of Law and Policy, 16
• Scheines, R. (2008). “Causation, Truth, and the Law”, Brooklyn Law Review, 73, 2,
• Hoyer, P., Hyvarinen, A., Scheines, R., Spirtes, P., Ramsey, J., Lacerda, G., Shimizu, S. (2008). Causal discovery of linear acyclic models with arbitrary distributions. Proceedings of the 24th Conference on Uncertainty and Artificial Intelligence, 2008, Helsinki, Finland.
• 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.
• 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.

Articles

• Cryder, C., Loewenstein, G., Scheines, R. (2013).  The Donor is in the Details, Organizational Behavior and Human Decision Processes, 120 (1), 15-23.
• 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
• Cooper, G., Bahar, I., Becich, M., Benos, P., Berg, J., Espino Glymour, C., Crowley R., Kienholz, M., Lee, A., Scheines, R., Lu, X., The Center for Causal Discovery of Biomedical Knowledge from Big Data, JAMA, (2015)
• Bonifay, W., S. Reise, R. Scheines, R. Meijer (2015). "When are multidimensional data unidimensional enough for structural equation modeling? An evaluation of the DETECT multidimensional index," Structural Equation Modeling. 22 (4), 504-516

Articles

• Yang, R., Spirtes, P., Scheines, R., Reise, S., Mansoff, M. (2017). Finding Pure Submodels for Improved Differentiation of Bifactor and Second-Order Models. Structural Equation Modeling, 24 (3), 402-413.
• Scheines, R., and Ramsey, J. (2017). Measurement Error and Causal Discovery. Proceedings of the UAI 2016 Workshop on Causation: Foundation to Application, pp. 1-7

• Lam, W. Y., Andrews, B., & Ramsey, J. (2022, August). Greedy relaxations of the sparsest permutation algorithm. In Uncertainty in Artificial Intelligence (pp. 1052-1062). PMLR.
• Andrew, B., Ramsey, J, Sanchez-Romero, R., Camchong, J., and Kummerfeld, E. Fast Scalable and Accurate Discovery of DAGs using the Best Order Score Search and Grow Shrink Trees (2023, October)—Neurips, In Publication.