Robin Evans

Robin Evans

Graphical models, causality, algebraic statistics

I am an Associate Professor and Fellow of Jesus College. My research interests are in graphical models, causality and algebraic statistics.

Publications

2020

  • R. J. Evans , Model selection and local geometry, Annals of Statistics, 2020.

2019

  • R. J. Evans , T. Richardson , Smooth, identifiable supermodels of discrete DAG models with latent variables, Bernoulli, vol. 25, no. 2, 848–876, 2019.
  • E. S. Allman , H. B. Cervantes , R. J. Evans , S. Hoşten , K. Kubjas , D. Lemke , J. A. Rhodes , P. Zwiernik , Maximum likelihood estimation of the latent class model through model boundary decomposition, Algebraic Statistics, vol. 10, no. 1, 51–84, 2019.

2018

  • R. J. Evans , Margins of discrete Bayesian networks, Annals of Statistics, vol. 46, no. 6A, 2623–2656, 2018.
  • I. Shpitser , R. J. Evans , T. S. Richardson , Acyclic Linear SEMs Obey the Nested Markov Property, in Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence (UAI-18), 2018, vol. 2018.

2017

  • C. Nowzohour , M. Maathuis , R. J. Evans , P. Bühlmann , Structure learning with bow-free acyclic path diagrams, Electronic Journal of Statistics, vol. 11, no. 2, 5342–5374, 2017.

2016

  • R. J. Evans , Graphs for margins of Bayesian networks, Scandinavian Journal of Statistics, vol. 43, no. 3, 625–648, 2016.
  • R. B. A. Silva , R. J. Evans , Causal Inference through a Witness Protection Program, Journal of Machine Learning Research, vol. 17, no. 56, 1–53, 2016.
  • A. Hitz , R. J. Evans , One-Component Regular Variation and Graphical Modeling of Extremes, Journal of Applied Probability, vol. 53, no. 3, 733–746, 2016.

2015

  • R. J. Evans , V. Didelez , Recovering from Selection Bias using Marginal Structure in Discrete Models, in Proceedings of Causal Inference Workshop, Uncertainty in Artificial Intelligence, 2015.
  • R. J. Evans , Conditional distributions and log-linear parameters, Electronic Journal of Statistics, vol. 9, no. 1, 475–491, 2015.

2014

  • R. J. Evans , T. S. Richardson , Markovian acyclic directed mixed graphs for discrete data, Annals of Statistics, vol. 42, no. 4, 1452–1482, 2014.

2013

  • R. J. Evans , A. Forcina , Two algorithms for fitting constrained marginal models, Computational Statistics and Data Analysis, vol. 66, 1–7, 2013.
  • I. Shpitser , T. Richardson , R. J. Evans , J. Robins , Sparse nested Markov models with log-linear parameters, in Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence (UAI-13), 2013, 576–585.
  • R. J. Evans , T. S. Richardson , Marginal log-linear parameters for graphical Markov models, Journal of the Royal Statistical Society: Series B, vol. 75, no. 4, 743–768, 2013.

2012

  • R. J. Evans , Graphical methods for inequality constraints in marginalized DAGs, in Machine Learning for Signal Processing, 2012.

2011

  • T. S. Richardson , R. J. Evans , J. M. Robins , Transparent parameterizations of models for potential outcomes, Bayesian Statistics, vol. 9, 569–610, 2011.

2010

  • R. J. Evans , T. S. Richardson , Maximum likelihood fitting of acyclic directed mixed graphs to binary data, in Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence (UAI-10), 2010, 177–184.