My google scholar page.

Publications

Kummerfeld, E. & Rix, A. & Anker, J. & Kushner, M. (2019). Assessing the Collective Utility of Multiple Analyses On Clinical Alcohol Use Disorder Data. Journal of the American Medical Informatics Association.

Kummerfeld, E. & Anker, J. & Rix, A. & Kushner, M. (2018). Methodological Advances in the Study of Hidden Variables: A Demonstration on Clinical Alcohol Use Disorder Data. AMIA Annual Symposium proceedings. AMIA Symposium, 2018, 710-719. PDF

Anker, J. & Kummerfeld, E. & Rix, A. & Burwell, S. & Kushner, M. (2018). Causal Network Modeling of the Determinants of Drinking Behavior in Comorbid Alcohol Use and Anxiety Disorder. Alcoholism, Clinical and Experimental Research, October. Link

Redish, A.D., & Kummerfeld, E. & Morris, R.L., & Love, A.C. (2018). Opinion: Reproducibility failures are essential to scientific inquiry. Proceedings of the National Academy of Sciences, 115(20), 5042-5046. Open Access
*In the top 5% of all research outputs scored by Altmetric

Kummerfeld, E., & Ramsey, J. (2016). Causal Clustering for 1-Factor Measurement Models. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’16, pages 1655-1664, New York, NY, USA, 2016. ACM. PDF Video

Kummerfeld, E., & Zollman, K. (2015). Conservatism and the scientific state of nature. British Journal for the Philosophy of Science.  PDF
* BJPS Editor’s Choice: article is open access.

Kummerfeld, E., & Ramsey, J. & Yang, R. & Spirtes, P. & Scheines, R. (2014). Causal Clustering for 2-Factor Measurement Models. In T. Calders, F. Esposito, E. Hullermeier, and R. Meo, editors, Machine Learning and Knowledge Discovery in Databases, volume 8725 of Lecture Notes in Computer Science, pages 34-49. Springer Berlin Heidelberg. PDF

Kummerfeld, E., & Danks, D. (2014). Model change and methodological virtues in scientific inference. Synthese, 191(12), 2673-2693. PDF
-Erratum for Model change and methodological virtues in scientific inference. PDF

Kummerfeld, E., & Danks, D. (2013). Tracking time-varying graphical structure. In C.J.C. Burges, L. Bottou, M. Welling, Z. Ghahramani, & K.Q. Weinberger (Eds.), Advances in neural information processing systems 26. La Jolla, CA: The NIPS Foundation.  PDF

Peer-reviewed Conference Talks:

Kummerfeld, E. & Anker, J. & Rix, A. & Kushner, M. (Forthcoming) “Methodological Advances in the Study of Hidden Variables: A Demonstration on Clinical Alcohol Use Disorder Data.” American Medical Informatics Association 2018 Annual Symposium. Slides

Kummerfeld, E., & Cooper, G. (2017) “A New Method for Estimating Causal Model Learning Accuracy.” The 4th Workshop on Data Mining for Medical Informatics: Causal Inference for Health Data Analytics. Slides Paper

Kummerfeld, E., & Ramsey, J. & Yang, R. & Spirtes, P. & Scheines, R. (2014) “Causal Clustering for 2-Factor Measurement Models.” The European Conference on Machine Learning. Slides

Kummerfeld, E., & Danks, D. (2013) “Model Selection, Decision Making, and Normative Pluralism: Theory and Climate Science Application.” The 6th Munich-Sydney-Tilburg conference on Models and Decisions. Slides

Invited Talks:

Kummerfeld, E. (2017) “New Methods for Discovering Hidden Causes from Observational Data.” University of Minnesota, School of Statistics.

Kummerfeld, E. (2016) “Learning Latent Variable Causal Models.” University of Minnesota, Institute for Health Informatics.

Kummerfeld, E. (2015) “Latent Variable Discovery.” University of Pittsburgh, School of Medicine, Special Lecture for the Department of Biomedical Informatics.

Kummerfeld, E. (2014) “Model selection and normative pluralism in climate science.” Carnegie Mellon University, Invited Lecture for the Summer School in Logic and Formal Epistemology.

Conference Posters I’ve Presented:

Kummerfeld, E., & Ramsey, J. (2016). “Causal Clustering for 1-Factor Measurement Models.” The International Conference on Knowledge Discovery and Data Mining

Kummerfeld, E., & Ramsey, J. & Yang, R. & Spirtes, P. & Scheines, R. (2014) “Causal Clustering for 2-Factor Measurement Models.” The European Conference on Machine Learning. Poster

Kummerfeld, E., & Danks, D. (2013) “Tracking time-varying graphical structure.” Neural Information Processing Systems. Poster

Kummerfeld, E., & Danks, D. (2012) “Online Learning of Time-varying Causal Structures.” Uncertainty in Artificial Intelligence Causal Structure Learning Workshop.

Other Documents

Kummerfeld, E. (2016) “How To Use Factor Clustering Algorithms.” PDF