My google scholar page.
Publications
Lee, K. & Kummerfeld, E. & Robinson, E. & Anderson, L. & Rantz, M. (forthcoming). Data-Driven Analytics to Discover APRN’s Impact on Nursing Home Hospitalization: Causal Discovery Analysis. Journal of the American Medical Directors Association.
Kummerfeld, E. & Andrews, B. (forthcoming). Beyond Integrative Experiment Design: Systematic Experimentation Guided by Causal Discovery AI. Behavioral and Brain Sciences.
Kummerfeld, E. & Lim, J. & Shi, X. (preprint). Data-driven Automated Negative Control Estimation (DANCE): Search for, Validation of, and Causal Inference with Negative Controls. Arxiv
Kummerfeld, E. & Jones, G. (2023). One Data Set, Many Analysts: Implications for Practicing Scientists. Frontiers in Psychology, 14, 487. Open Access
Pierce, B. & Kirsh, T. & Ferguson, A.R. & Neylan, T.C. & Ma, S. & Kummerfeld, E. & Cohen, B.E. & Nielson, J.L. (2023). Causal discovery replicates symptomatic and functional interrelations of posttraumatic stress across five patient populations. Frontiers in Psychiatry, 13, 3102. Open Access
Sun, J. & Peng, L. & Li, T. & Adila, D. & Zaiman, Z. & Melton-Meaux, G. B. & Ingraham, N. E. & Murray, E. & Boley, D. & Switzer, S. & Burns, J. L. & Huang, K. & Allen, T. & Steenburg, S. D. & Gichoya, J. W. & Kummerfeld, E.* & Tignanelli, C. J.* (2022). Performance of a Chest Radiograph AI Diagnostic Tool for COVID-19: A Prospective Observational Study. Radiology. Artificial intelligence, 4(4), e210217. Free Access
*contributed equally as senior authors to this manuscript.
Niklason, G.R. & Rawls, E. & Ma, S. & Kummerfeld, E. & Maxwell, A.M. & Brucar, L.R. & Drossel, G. & Zilverstand, A. (2022) Explainable machine learning analysis reveals sex and gender differences in the phenotypic and neurobiological markers of Cannabis Use Disorder. Sci Rep 12, 15624. Open Access
Stevenson, B.L. & Kummerfeld, E. & Merrill, J.E. & Blevins, C. & Abrantes, A.M. & Kushner, M.G. & Lim, K.O. (2022) Quantifying heterogeneity in mood–alcohol relationships with idiographic causal models. Alcoholism: Clinical and Experimental Research, 00, 1– 12. Open Access
Rawls, E. & Kummerfeld, E. & Mueller, B.A & Ma, S. & Zilverstand, A. (2022). The Resting-State Causal Human Connectome is Characterized by Hub Connectivity of Executive and Attentional Networks. NeuroImage, 255, 119211. Open Access
Bronstein, M.V. & Everaert, J. & Kummerfeld, E. & Haynos, A.F. & Vinogradov, S. (2022). Biased and inflexible interpretations of ambiguous social situations: Associations with eating disorder symptoms and socioemotional functioning. International Journal of Eating Disorders, 55( 4), 518– 529. SSRN
Bronstein, M.V. & Kummerfeld, E. & MacDonald III, A. & Vinogradov, S. (2022). Willingness to Vaccinate Against SARS-CoV-2: The Role of Reasoning Biases and Conspiracy Theories. Vaccine, 40(2), 213–222. SSRN
Proceedings of The 2021 Causal Analysis Workshop Series, PMLR 160:52-54. Open Access
Stevenson, B. & Kummerfeld, E. & Merrill, J. (2021) Applying Causal Discovery to Intensive Longitudinal Data. Proceedings of The 2021 Causal Analysis Workshop Series, PMLR 160:20-29. Open Access
Miley, K. & Meyer-Kalos, P. & Ma, S., Bond & D., Kummerfeld, E.* & Vinogradov, S.* (2021). Causal pathways to social and occupational functioning in the first episode of schizophrenia: Uncovering unmet treatment needs. Psychological Medicine, 1-9. doi:10.1017/S0033291721003780 Open Access
*contributed equally as senior authors to this manuscript.
Bhavnani, S.K. & Kummerfeld, E. & Zhang, W. & Kuo, Y. F. & Garg, N. & Visweswaran, S. & Raji, M. & Radhakrishnan, R. & Golvoko, G. & Hatch, S. & Usher, M. & Melton-Meaux, G. & Tignanelli, C. (2021). Heterogeneity in COVID-19 Patients at Multiple Levels of Granularity: From Biclusters to Clinical Interventions. AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2021, 112–121. PMC
Musa, G.J. & Geronazzo-Alman, L. & Fan, B. & Cheslack-Postava, K. & Bavley, R. & Wicks, J. & Bresnahan, M. & Amsel, L., Fiano, E. & Saxe, G. & Kummerfeld, E. & Ma, S. & Hoven, C.W. (2021) Neighborhood characteristics and psychiatric disorders in the aftermath of mass trauma: A representative study of New York City public school 4th-12th graders after 9/11. J Psychiatr Res. 2021 Jun;138:584-590. doi: 10.1016/j.jpsychires.2021.05.002. Epub 2021 May 7. PMID: 33992981. Link
Kummerfeld, E. (2021) A simple interpretation of undirected edges in essential graphs is wrong. PLOS ONE 16(4): e0249415. Open Access
Rawls, E. & Kummerfeld, E. & Zilverstand, A. (2021) An Integrated Multimodal Model of Alcohol Use Disorder Generated by Data-Driven Causal Discovery Analysis. Commun Biol 4, 435. Open Access
Kummerfeld, E. & Ma, S. & Blackman, R.K. & DeNicola, A.L. & Redish, A.D. & Vinogradov, S. & Crowe, D.A. & Chafee, M.V. (2020) Cognitive control errors in nonhuman primates resembling those in schizophrenia reflect opposing effects of NMDAR blockade on causal interactions between cells and circuits in prefrontal and parietal cortex. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging. 2020 Jul 1;5(7):705-14. Full Text
Tignanelli, C.J. & Rix, A. & Napolitano, L.M. & Hemmila, M.R. & Ma, S. & Kummerfeld, E. (2020) Adherence to Evidence-based Practices for Treatment of Patients With Traumatic Rib Fractures and Associated Mortality Rates Among US Trauma Centers. JAMA Netw Open. 2020;3(3):e201316. Full Text
Morris, R.S. & Milia, D. & Glover, J. & Napolitano, L.M. & Chen, B. & Lindemann, E. & Hemmila, M.R. & Stein, D. & Kummerfeld, E. & Chipman, J. & Tignanelli, C.J. (2020). Predictors of Elderly Mortality After Trauma: A Novel Outcome Score. Journal of Trauma and Acute Care Surgery, 88(3) pp. 416-424.
Kummerfeld, E. & Rix, A. (2019). Simulations evaluating resampling methods for causal discovery: ensemble performance and calibration. 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), San Diego, CA, USA, 2019, pp. 2586-2593. Preprint
Deckert, A. & Kummerfeld, E. (2019). Investigating the effect of binning on causal discovery. 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), San Diego, CA, USA, 2019, pp. 2574-2581. Preprint
Ma, S. & Kirsh, T. & Kummerfeld, E. & Fisher, M. & Schermitzler, B. & Vinogradov, S. (2019). Constructing Instrument Mapping Using Markov Boundary Techniques: A Case Study with Two Quality of Life Instruments. 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), San Diego, CA, USA, 2019, pp. 2594-2598.
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. Preprint
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
* Top Downloaded Paper in Alcoholism: Clinical and Experimental Research 2018-2019.
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:
Stevenson, B. & Kummerfeld, E. & Merrill, J. (2021) “Applying Causal Discovery to Intensive Longitudinal Data.” CAWS 2021.
Kummerfeld, E. & Rix, A. (2019). Simulations evaluating resampling methods for causal discovery: ensemble performance and calibration. 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM).
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.” 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. (2020) “Methods for Using Observational Data to Answer Causal Questions.” Open Data Science Conference East 2020.
Kummerfeld, E. & Rix, A. (2019) “Evaluating Resampling Methods For Validating Data-Driven Causal Structures.” University of Minnesota, Institute for Research in Statistics and its Applications. Slides
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. (2020) “Causal Analysis Provides a Toolbelt, Not a Silver Bullet.” Blog post on ODSC. Link
Kummerfeld, E. (2016) “How To Use Factor Clustering Algorithms.” PDF
Erich Kummerfeld 