The TETRAD Project

Causal Models and Statistical Data


Recent Publications

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.

Hanson, C., S. J. Hanson, J.D. Ramsey, and C. Glymour (2013). Atypical Effective Connectivity of Social Brain Networks in Individuals with Autism. Brain connectivity, (online)

Livengood, J., Sytsma, Feltz, A., Scheines, R., and Machery, E (2010). Philosophical Temperament, chapter 1.3 in Philosophical Psychology, 23: 3, 313-330.

Mumford, J.A., J.D. Ramsey (2013). Bayesian networks for fMRI: A primer. NeuroImage, (Online)

Ramsey, J. D. (2015). Scaling up Greedy Causal Search for Continuous Variables. Tech Report, Center for Causal Discovery.

Ramsey, Joseph D., Stephen J. Hanson, Clark Glymour (2011). Multi-subject search correctly identifies causal connections and most causal directions in the DCM models of the Smith et al. simulation study. NeuroImage, Volume 58, pp. 838-848

Ramsey, J.D., S.J. Hanson, C. Hanson, Y.O. Halchenko, R.A. Poldrack, and C. Glymour (2010). Six problems for causal inference from fMRI. NeuroImage, Volume 49, pp. 1545-1558.

Ramsey, J.D., P. Spirtes, C. Glymour (2011). On meta-analyses of imaging data and the mixture of records. NeuroImage, Volume 57, pp.323-330.

Ramsey, J.D., R. Sanchez-Romero, C. Glymour (2014). Non-Gaussian methods and high-pass filters in the estimation of effective connections. NeuroImage, Volume 84, pp.986-1006.

Smith, S. M., K. L. Miller, G. Salimi-Khorshidi, M. Webster, C. F. Beckmann, T. E. Nichols, J. D. Ramsey, and M. W. Woolrich (2011). Network modelling methods for FMRI. NeuroImage, Volume 54, pp.875-891.

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.

Older Publications

Ali, R., Richardson, T., Spirtes, P., and Zhang, J. (2005) Towards Characterizing Markov Equivalence Classes for Directed Acyclic Graph Models with Latent Variables, Uncertainty in Artificial Intelligence 2005, Edinboro, Scotland.

Bai, X., C. Glymour, R. Padman, J. Ramsey, P. Spirtes, and F. Wimberly. PCX: Markov Blanket Classification for Large Data Sets with Few Cases (2004). Center for Automated Learning and Discovery, CMU-CALD-04-102, School of Computer Science, Carnegie Mellon University.

Burgansky-Eliash Z, G. Wollstein, T. Chu, J. Ramsey, C. Glymour, R. Noecker, and J. Schuman (2005). Optical coherence tomography machine learning classifiers for glaucoma detection. Investigative Ophthamology & Visual Science, 2005;46:4147-4152.

Chu, T., Glymour, C., Scheines, R., Spirtes, P. (2003) A Statistical Problem for Inference to Regulatory Structure from Associations of Gene Expression Measurements with Microarrays, Bioinformatics, 19, pp. 1147-1152.

Eberhardt, F., Glymour, C., & Scheines, R. (2006). N-1 Experiments Suffice to Determine the Causal Relations Among N Variables, 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.

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, 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.

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

Gerdes, D. A., C. Glymour, J. D. Ramsey. (2006). Who's Calling? Deriving Organization Struc ture from Communication Records. In A. Kott (Ed)., Information Warfare and Organizational Decision-Making. Boston, MA: Artech House.

C. Hanson, S. Hanson, J. Ramsey, C. Glymour. Atypical Effective Connectivity of Social Brain Networks in Individuals with Autism. Brain Connect. 2013 Oct 4.

Mumford, J. and Ramsey, J. (2014). Bayesian networks for fMRI: A primer. NeuroImage 86:573-582.

Ramsey, Joseph (2001). "Mixture and Expertise in Automatic Causal Discovery." Ph.D. diss., University of California at San Diego.

Ramsey, J. (2006). A PC-style Markov blanket search for high-dimensional datasets. Technical Report, CMU-PHIL-177, Carnegie Mellon University, Department of Philosophy.

Ramsey, Joseph, Paul Gazis, Ted Roush, Peter Spirtes and Clark Glymour (2002). "Automated Remote Sensing with Near Infrared Reflectance Spectra: Carbonate Recognition." Data Mining and Knowledge Discovery, v. 6, no. 3, 275-291.

Ramsey, J., P. Spirtes, and J. Zhang (2006). Adjacency-Faithfulness and Conservative Causal Inference. Proceedings of the 22nd Conference on Uncertainty in Artificial Intelligence, 401-408, Oregon, AUAI Press.

Ramsey, J., Zhang, J., and Spirtes, P., (2006) Adjacency-Faithfulness and Conservative Causal Inference, Uncertainty in Artificial Intelligence 2006, Boston, MA.

J. Ramsey, R. Sanchez-Romero, C. Glymour (2013). Non-Gaussian methods and high-pass filters in the estimation of effective connections, NeuroImage., http://dx.doi.org/10.1016/j.meuroimage.2013.09.0.

J. Ramsey, P. Spirtes, C. Glymour On meta-analyses of imaging data and the mixture of records. NeuroImage, Volume 57, Issue 2, 15 July 2011, Pages 323-330.

Richardson, T., Spirtes, P. (2002) Ancestral Graph Markov Models, Annals of Statistics, 2002, 30 pp. 962-1030.

Richardson, T., and Spirtes, P. (2000) Ancestral Markov Graphical Models, University of Washington Department of Statistics Technical Report 375.

Richardson, T., and Spirtes, P. (1996). Automated discovery of linear feedback models, Technical Report CMU-75-Phil.

Robins, J., Scheines, R., Spirtes, P., and Wasserman, L. (2003). Uniform Consistency in Causal Inference, Biometrika, September, 90: pp. 491-515.

Robins, J., Scheines, R., Spirtes, P., and Wasserman, L. (2000) Uniform Consistency in Causal Inference, Carnegie Mellon University Department of Statistics Technical Report 725.

Scheines, R. (2008), Causation, Truth, and the Law, Brooklyn Law Review, 73, 2

Scheines, R. (forthcoming). An Introduction to Causal Inference, in Causality in Crisis, ed. by Steven Turner and Vaughan McKim, University of Notre Dame Press.

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.

Scheines, R., Cooper, G., Changwon Yoo,Tianjiao Chu. (2001). Piece-wise Linear Instrumental Variable Estimation of Causal Influence, in Proceedings of Eighth International Workshop on Artificial Intelligence and Statistics, Morgan Kauffman.

Scheines, R., Hoijtink, H., & Boomsma, A. (1999). Bayesian Estimation and Testing of Structural Equation Models. Psychometrika. 64, 1, pp. 37-52.

Scheines, Richard and Joseph Ramsey (2001). "Simulating Genetic Regulatory Networks." Technical Report CMU-PHIL-24, Philosophy Department, Carnegie Mellon University.

Scheines, R., Spirtes, P., Glymour, C., Meek, C., and Richardson, T. (forthcoming). The TETRAD Project: Constraint Based Aids to Causal Model Specification, Multivariate Behavioral Research.

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.

Silva, R., Scheines, R., Glymour, C., and Spirtes. P. (2003). Learning Measurement Models for Unobserved Variables, in Proceedings of the Nineteenth Conference on Uncertainty in Artificial Intelligence, U. Kjaerulff and C. Meek, eds., Morgan Kauffman.

Spirtes, P. (2000) An Anytime Algorithm for Causal Inference, to be presented at AI and Statistics 2001.

Spirtes, P. (1995). Directed Cyclic Graphical Representation of Feedback Models, Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence, ed. by Philippe Besnard and Steve Hanks, Morgan Kaufmann Publishers, Inc., San Mateo, 1995.

Spirtes, P. (2005) Graphical Models, Causal Inference, and Econometric Models, Journal of Economic Methodology. 2005 12:1, pp. 1-33.

Spirtes, P. (1997). Limits on Causal Inference from Statistical Data, presented at American Economics Association Meeting.

Spirtes, P. (2008) Variable Definition and Causal Inference, (forthcoming) Proceedings of the 13th International Congress of Logic Methodology and Philosophy of Science.

Spirtes, P., Cooper, G. (1997). An Experiment in Causal Discovery Using a Pneumonia Database, Proceedings of AI and Statistics 99.

Spirtes, P., Glymour, C. and Scheines, R. (2000). Causation, Prediction, and Search, 2nd ed. New York, N.Y.: MIT Press.

Spirtes, P., Glymour, C., and Scheines, R. (2000) Constructing Bayesian Network Models of Gene Expression Networks from Microarray Data, to appear in the Proceedings of the Atlantic Symposium on Computational Biology, Genome Information Systems & Technology.

Spirtes, P., Glymour, C., and Scheines, R. Kauffman, S.,Aimale, V., & 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.

Spirtes, P., Meek, C., and Richardson, T. (1996). Causal Inference in the Presence of Latent Variables and Selection Bias, Technical Report CMU-77-Phil.

Spirtes, P., and Richardson, T. (1996). A Polynomial Time Algorithm For Determining DAG Equivalence in the Presence of Latent Variables and Selection Bias, Proceedings of the 6th International Workshop on Artificial Intelligence and Statistics.

Spirtes, P., Richardson, T., Meek, C. (1997). The Dimensionality of Mixed Ancestral Graphs, Technical Report CMU-83-Phil.

Spirtes, P., Richardson, T., and Meek, C. (1996). Heuristic Greedy Search Algorithms for Latent Variable Models, Proceedings of the 6th International Workshop on Artificial Intelligence and Statistics.

Spirtes, P., Richardson, T., Meek, C., Scheines, R., and Glymour, C. (1997). t as a Structural Equation Modelling Tool, Technical Report CMU-82-Phil.

Spirtes, P., and Scheines, R. (2004). Causal Inference of Ambiguous Manipulations, in Proceedings of the Philosophy of Science Association Meetings, 2002.

Spirtes, P., and Scheines, R. (forthcoming). Reply to Freedman, in Causality in Crisis, ed. by Steven Turner and Vaughan McKim, University of Notre Dame Press.

Spirtes, P., Richardson, T., Meek, C., Scheines, R., and Glymour, C., (1996). Using D-separation to Calculate Zero Partial Correlations in Linear Models with Correlated Errors, Technical Report CMU-72-Phil.

Wimberly, F., T. Heiman, C. Glymour, and J. Ramsey (2003). Experiments on the Accuracy of Algorithms for Inferring the Structure of Genetic Regulatory Networks from Microarray Expression Levels. Proceedings of the Workshop on Learning Graphical Models in Computational Genomics, International Joint Conference on Artificial Intelligence, Acapulco.

Zhang, J., and Spirtes, P. (forthcoming) Detection of Unfaithfulness and Robust Causal Inference, Minds and Machines.

Zhang, J., and Spirtes, P. (2003) Strong Faithfulness and Uniform Consistency in Causal Inference, UAI '03, Proceedings of the 19th Conference in Uncertainty in Artificial Intelligence, August 7-10 2003, Acapulco, Mexico, ed. by Christopher Meek and Uffe Kjarulff, Morgan Kaufmann.

Zhang, J., and Spirtes, P. (2005) A Transformational Characterization of Markov Equivalence for Directed Acyclic Graphs with Latent Variables, Uncertainty in Artificial Intelligence 2005, Edinboro, Scotland.