Robin Evans

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Thomas S. RichardsonUniversity of WashingtonVerified email at uw.edu
Ilya ShpitserJohn C. Malone Associate Professor, Department of Computer Science, Johns Hopkins UniversityVerified email at cs.jhu.edu
James M. Robinsprofessor of epidemiology and biostatistics harvard school of public healthVerified email at hsph.harvard.edu
Daniel FreemanProfessor of Clinical Psychology, University of OxfordVerified email at psych.ox.ac.uk
Peter BühlmannProfessor of Statistics and Mathematics, ETH ZurichVerified email at stat.math.ethz.ch
Felicity WaiteResearch Clinical Psychologist, University of OxfordVerified email at psych.ox.ac.uk
Zhongyi HuUniversity of OxfordVerified email at keble.ox.ac.uk
Dino SejdinovicProfessor of Statistical Machine Learning, University of AdelaideVerified email at adelaide.edu.au
Jake FawkesUniversity Of OxfordVerified email at stats.ox.ac.uk
Ricardo SilvaProfessor of Statistical Machine Learning and Data Science, Department of Statistical Science, UCLVerified email at stats.ucl.ac.uk
Vanessa DidelezProfessor of Statistics and Causal Inference, Leibniz Inst f Prevention Res and Epidemiology - BIPSVerified email at leibniz-bips.de
Emma ČernisUniversity of BirminghamVerified email at bham.ac.uk
Anke EhlersUniversity of OxfordVerified email at psy.ox.ac.uk
Bao Sheng LoeUniversity of CambridgeVerified email at cam.ac.uk
William RudgardUniversity of OxfordVerified email at spi.ox.ac.uk
antonio forcinaDipartimenti di Economia, Università di PerugiaVerified email at stat.unipg.it
Adrien S. HitzMaterialize.X & AHX.aiVerified email at materializex.com
Bohao YaoUniversity of OxfordVerified email at stats.ox.ac.uk
Piotr ZwiernikUniversity of TorontoVerified email at utoronto.ca
Kaie KubjasAssistant Professor of Mathematics, Aalto UniversityVerified email at aalto.fi

Robin Evans

Professor of Statistical Science, University of Oxford

Verified email at stats.ox.ac.uk - Homepage

Causality graphical models algebraic statistics


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Nested Markov properties for acyclic directed mixed graphs TS Richardson, RJ Evans, JM Robins, I Shpitser The Annals of Statistics 51 (1), 334-361, 2023	126	2023
Graphs for margins of Bayesian networks RJ Evans Scandinavian Journal of Statistics 43 (3), 625-648, 2016	92	2016
Markovian acyclic directed mixed graphs for discrete data RJ Evans, TS Richardson	74	2014
Introduction to nested Markov models I Shpitser, RJ Evans, TS Richardson, JM Robins Behaviormetrika 41, 3-39, 2014	74	2014
Margins of discrete Bayesian networks RJ Evans	73	2018
Dissociation in relation to other mental health conditions: An exploration using network analysis E Černis, R Evans, A Ehlers, D Freeman Journal of Psychiatric Research 136, 460-467, 2021	65	2021
Transparent parameterizations of models for potential outcomes TS Richardson, RJ Evans, JM Robins Bayesian statistics 9, 569-610, 2011	64	2011
Adolescent paranoia: Prevalence, structure, and causal mechanisms JC Bird, R Evans, F Waite, BS Loe, D Freeman Schizophrenia bulletin 45 (5), 1134-1142, 2019	56	2019
Graphical methods for inequality constraints in marginalized DAGs RJ Evans 2012 IEEE International Workshop on Machine Learning for Signal Processing, 1-6, 2012	53	2012
Marginal log-linear parameters for graphical Markov models RJ Evans, TS Richardson Journal of the Royal Statistical Society Series B: Statistical Methodology …, 2013	41	2013
Distributional equivalence and structure learning for bow-free acyclic path diagrams C Nowzohour, MH Maathuis, RJ Evans, P Bühlmann	35	2017
Two algorithms for fitting constrained marginal models RJ Evans, A Forcina Computational statistics & Data analysis 66, 1-7, 2013	35	2013
Smooth, identifiable supermodels of discrete DAG models with latent variables RJ Evans, TS Richardson	30	2019
Maximum likelihood fitting of acyclic directed mixed graphs to binary data RJ Evans, TS Richardson arXiv preprint arXiv:1203.3479, 2010	28	2010
Parameter and structure learning in nested Markov models I Shpitser, TS Richardson, JM Robins, R Evans arXiv preprint arXiv:1207.5058, 2012	25	2012
Acyclic linear SEMs obey the nested Markov property I Shpitser, RJ Evans, TS Richardson Uncertainty in artificial intelligence: proceedings of the... conference …, 2018	20	2018
Maximum likelihood estimation of the latent class model through model boundary decomposition ES Allman, HB Cervantes, R Evans, S Hoşten, K Kubjas, D Lemke, ... arXiv preprint arXiv:1710.01696, 2017	19	2017
Causal inference through a witness protection program R Silva, R Evans Journal of Machine Learning Research 17 (56), 1-53, 2016	18	2016
Model selection and local geometry RJ Evans The Annals of Statistics 48 (6), 3513-3544, 2020	17	2020
Faster algorithms for Markov equivalence Z Hu, R Evans Conference on Uncertainty in Artificial Intelligence, 739-748, 2020	16	2020

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