| Network modelling methods for FMRI SM Smith, KL Miller, G Salimi-Khorshidi, M Webster, CF Beckmann, ... Neuroimage 54 (2), 875-891, 2011 | 2051 | 2011 |
| Six problems for causal inference from fMRI JD Ramsey, SJ Hanson, C Hanson, YO Halchenko, RA Poldrack, ... neuroimage 49 (2), 1545-1558, 2010 | 386 | 2010 |
| A million variables and more: the fast greedy equivalence search algorithm for learning high-dimensional graphical causal models, with an application to functional magnetic … J Ramsey, M Glymour, R Sanchez-Romero, C Glymour International journal of data science and analytics 3, 121-129, 2017 | 338 | 2017 |
| Adjacency-faithfulness and conservative causal inference J Ramsey, J Zhang, PL Spirtes arXiv preprint arXiv:1206.6843, 2012 | 310 | 2012 |
| Optical coherence tomography machine learning classifiers for glaucoma detection: a preliminary study Z Burgansky-Eliash, G Wollstein, T Chu, JD Ramsey, C Glymour, ... Investigative ophthalmology & visual science 46 (11), 4147-4152, 2005 | 243 | 2005 |
| Discovering cyclic causal models by independent components analysis G Lacerda, PL Spirtes, J Ramsey, PO Hoyer arXiv preprint arXiv:1206.3273, 2012 | 203 | 2012 |
| A hybrid causal search algorithm for latent variable models JM Ogarrio, P Spirtes, J Ramsey Conference on probabilistic graphical models, 368-379, 2016 | 192 | 2016 |
| Causal discovery from heterogeneous/nonstationary data B Huang, K Zhang, J Zhang, J Ramsey, R Sanchez-Romero, C Glymour, ... Journal of Machine Learning Research 21 (89), 1-53, 2020 | 184 | 2020 |
| Bayesian networks for fMRI: a primer JA Mumford, JD Ramsey Neuroimage 86, 573-582, 2014 | 152 | 2014 |
| Multi-subject search correctly identifies causal connections and most causal directions in the DCM models of the Smith et al. simulation study JD Ramsey, SJ Hanson, C Glymour NeuroImage 58 (3), 838-848, 2011 | 138 | 2011 |
| Actual causation: a stone soup essay C Glymour, D Danks, B Glymour, F Eberhardt, J Ramsey, R Scheines, ... Synthese 175, 169-192, 2010 | 105 | 2010 |
| Causal discovery of linear acyclic models with arbitrary distributions PO Hoyer, A Hyvarinen, R Scheines, PL Spirtes, J Ramsey, G Lacerda, ... arXiv preprint arXiv:1206.3260, 2012 | 89 | 2012 |
| Causal clustering for 1-factor measurement models E Kummerfeld, J Ramsey Proceedings of the 22nd ACM SIGKDD international conference on knowledge …, 2016 | 77 | 2016 |
| Tetrad—a toolbox for causal discovery JD Ramsey, K Zhang, M Glymour, RS Romero, B Huang, I Ebert-Uphoff, ... 8th international workshop on climate informatics, 1-4, 2018 | 76 | 2018 |
| Estimating feedforward and feedback effective connections from fMRI time series: Assessments of statistical methods R Sanchez-Romero, JD Ramsey, K Zhang, MRK Glymour, B Huang, ... Network Neuroscience 3 (2), 274-306, 2019 | 70 | 2019 |
| Mixed graphical models for integrative causal analysis with application to chronic lung disease diagnosis and prognosis AJ Sedgewick, K Buschur, I Shi, JD Ramsey, VK Raghu, DV Manatakis, ... Bioinformatics 35 (7), 1204-1212, 2019 | 68 | 2019 |
| Scoring Bayesian networks of mixed variables B Andrews, J Ramsey, GF Cooper International journal of data science and analytics 6, 3-18, 2018 | 65 | 2018 |
| Comparison of strategies for scalable causal discovery of latent variable models from mixed data VK Raghu, JD Ramsey, A Morris, DV Manatakis, P Sprites, ... International journal of data science and analytics 6, 33-45, 2018 | 62 | 2018 |
| Non-Gaussian methods and high-pass filters in the estimation of effective connections JD Ramsey, R Sanchez-Romero, C Glymour Neuroimage 84, 986-1006, 2014 | 62 | 2014 |
| Mu2e conceptual design report RJ Abrams Lawrence Berkeley National Lab.(LBNL), Berkeley, CA (United States), 2012 | 61 | 2012 |
