Robin Evans

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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, no. 6, 3514–3544, 2020.
  

  @article{evans20,
    author = {Evans, R. J.},
    journal = {Annals of Statistics},
    title = {Model selection and local geometry},
    year = {2020},
    issue = {6},
    pages = {3514--3544},
    archiveprefix = {arXiv},
    eprint = {1801.08364}
  }
  

• Z. Hu , R. J. Evans , Faster algorithms for Markov equivalence, in Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence (UAI-20), 2020, vol. 2020.
  

  @inproceedings{hu20,
    author = {Hu, Z. and Evans, R. J.},
    title = {Faster algorithms for {M}arkov equivalence},
    booktitle = {Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence (UAI-20)},
    volume = {2020},
    year = {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.
  
  @article{evans19,
    author = {Evans, R. J. and Richardson, T.\\~{}S.},
    journal = {Bernoulli},
    volume = {25},
    number = {2},
    pages = {848--876},
    title = {Smooth, identifiable supermodels of discrete {DAG} models with latent variables},
    year = {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.
  

  @article{allman:19,
    title = {Maximum likelihood estimation of the latent class model through model boundary decomposition},
    author = {Allman, Elizabeth S and Cervantes, Hector Banos and Evans, R. J. and Ho{\c{s}}ten, Serkan and Kubjas, Kaie and Lemke, Daniel and Rhodes, John A and Zwiernik, Piotr},
    journal = {Algebraic Statistics},
    volume = {10},
    number = {1},
    pages = {51--84},
    year = {2019}
  }
  

2018

• R. J. Evans , Margins of discrete Bayesian networks, Annals of Statistics, vol. 46, no. 6A, 2623–2656, 2018.
  

  @article{evans:18,
    author = {Evans, R. J.},
    title = {Margins of discrete {B}ayesian networks},
    journal = {Annals of Statistics},
    volume = {46},
    number = {6A},
    pages = {2623--2656},
    archiveprefix = {arXiv},
    eprint = {1501.02103},
    year = {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.
  

  @inproceedings{shpitser:18,
    title = {Acyclic Linear {SEMs} Obey the Nested {M}arkov Property},
    author = {Shpitser, Ilya and Evans, R. J. and Richardson, Thomas S},
    booktitle = {Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence (UAI-18)},
    volume = {2018},
    year = {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.
  

  @article{nowzohour:17,
    title = {Structure learning with bow-free acyclic path diagrams},
    author = {Nowzohour, Christopher and Maathuis, Marloes and Evans, R. J. and B{\"u}hlmann, Peter},
    journal = {Electronic Journal of Statistics},
    volume = {11},
    number = {2},
    pages = {5342-5374},
    year = {2017}
  }
  

2016

• R. J. Evans , Graphs for margins of Bayesian networks, Scandinavian Journal of Statistics, vol. 43, no. 3, 625–648, 2016.
  

  @article{evans:mdags,
    author = {Evans, R. J.},
    journal = {Scandinavian Journal of Statistics},
    title = {Graphs for margins of {B}ayesian networks},
    archiveprefix = {arXiv},
    eprint = {1408.1809},
    volume = {43},
    issue = {3},
    pages = {625--648},
    year = {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.
  

  @article{silva:evans:16,
    title = {Causal Inference through a Witness Protection Program},
    author = {Silva, R. B. A. and Evans, R. J.},
    journal = {Journal of Machine Learning Research},
    volume = {17},
    issue = {56},
    pages = {1--53},
    year = {2016},
    archiveprefix = {arXiv},
    eprint = {1406.0531}
  }
  

• 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.
  

  @article{hitz:evans:16,
    title = {One-Component Regular Variation and Graphical Modeling of Extremes},
    author = {Hitz, A. and Evans, R. J.},
    journal = {Journal of Applied Probability},
    year = {2016},
    volume = {53},
    issue = {3},
    pages = {733-746}
  }
  

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.
  

  @inproceedings{evans:15,
    author = {Evans, R. J. and Didelez, V.},
    booktitle = {Proceedings of Causal Inference Workshop, Uncertainty in Artificial Intelligence},
    title = {Recovering from Selection Bias using Marginal Structure in Discrete Models},
    year = {2015}
  }
  

• R. J. Evans , Conditional distributions and log-linear parameters, Electronic Journal of Statistics, vol. 9, no. 1, 475–491, 2015.
  

  @article{evans15,
    author = {Evans, R. J.},
    journal = {Electronic Journal of Statistics},
    title = {Conditional distributions and log-linear parameters},
    volume = {9},
    issue = {1},
    pages = {475--491},
    year = {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.
  

  @article{evans:14,
    author = {Evans, R. J. and Richardson, T. S.},
    journal = {Annals of Statistics},
    number = {4},
    pages = {1452--1482},
    archiveprefix = {arXiv},
    eprint = {1301.6624},
    title = {Markovian acyclic directed mixed graphs for discrete data},
    volume = {42},
    year = {2014}
  }
  

2013

• R. J. Evans , A. Forcina , Two algorithms for fitting constrained marginal models, Computational Statistics and Data Analysis, vol. 66, 1–7, 2013.
  

  @article{evans:forcina:13,
    author = {Evans, R. J. and Forcina, A.},
    journal = {Computational Statistics and Data Analysis},
    pages = {1--7},
    title = {Two algorithms for fitting constrained marginal models},
    volume = {66},
    year = {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.
  

  @inproceedings{shpitser:13,
    author = {Shpitser, I. and Richardson, T.\\~{}S. and Evans, R. J. and Robins, J.\\~{}M.},
    booktitle = {Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence (UAI-13)},
    title = {Sparse nested Markov models with log-linear parameters},
    pages = {576-585},
    year = {2013}
  }
  

• 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.
  

  @article{evans:13,
    title = {Marginal log-linear parameters for graphical {M}arkov models},
    author = {Evans, R. J. and Richardson, T. S.},
    journal = {Journal of the Royal Statistical Society: Series B},
    volume = {75},
    number = {4},
    pages = {743--768},
    year = {2013},
    publisher = {Wiley Online Library}
  }
  

2012

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

  @inproceedings{evans:12,
    author = {Evans, R. J.},
    booktitle = {Machine Learning for Signal Processing},
    organization = {IEEE},
    title = {Graphical methods for inequality constraints in marginalized {DAGs}},
    year = {2012}
  }
  

• I. Shpitser , T. Richardson , J. M. Robins , R. J. Evans , Parameter and Structure Learning in Nested Markov Models, in UAI Workshop on Structure Learning, 2012.
  

  @inproceedings{shpitser:12,
    author = {Shpitser, I. and Richardson, T.S. and Robins, J. M. and Evans, R. J.},
    booktitle = {UAI Workshop on Structure Learning},
    title = {Parameter and Structure Learning in Nested {M}arkov Models},
    year = {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.
  

  @article{richardson:11,
    title = {Transparent parameterizations of models for potential outcomes},
    author = {Richardson, T. S. and Evans, R. J. and Robins, J. M.},
    journal = {Bayesian Statistics},
    volume = {9},
    pages = {569--610},
    year = {2011},
    publisher = {Oxford University Press New York}
  }
  

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.
  

  @inproceedings{evans:10,
    author = {Evans, R. J. and Richardson, T. S.},
    booktitle = {Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence (UAI-10)},
    pages = {177-184},
    title = {Maximum likelihood fitting of acyclic directed mixed graphs to binary data},
    year = {2010}
  }