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.



  • R. J. Evans, Margins of discrete Bayesian networks, Annals of Statistics, 2017.


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


  • R. J. Evans, Conditional distributions and log-linear parameters, Electronic Journal of Statistics, vol. 9, no. 1, 475–491, 2015.
  • R. J. Evans, Smooth, identifiable supermodels of discrete DAG models with latent variables, 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, T. S. Richardson, Markovian acyclic directed mixed graphs for discrete data, Annals of Statistics, vol. 42, no. 4, 1452–1482, 2014.


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


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


  • 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.