Professor Robin Evans

Professor of Statistical Science, Deputy Head of Department

Biographical Sketch

I received my PhD in Statistics from the University of Washington in 2011, and was a Postdoctoral Research Fellow at the Statistical Laboratory in Cambridge from 2011 to 2013.


Research Interests

My research is in understanding how multivariate statistical models – such as Bayesian network models, marginal models and latent variable models – can be used to learn about the world around us. In addition, I am interested in how these methods can be applied to epidemiology, medicine and the social sciences.

This work has led in various directions: finding implications of models that can be tested in data, and showing that no further constraints exist; determining whether quantities of scientific interest are identifiable; understanding when marginal models can be properly specified, simulated from, and fitted; and understanding when efficient model selection is possible.  A particular area of interest recently has been simulating from marginal causal models, and applications to combining evidence from different types of study.

I am also interested in the mathematical and statistical properties of more general latent variable models, conditional independence, and model parametrizations.


Ter-Minassian, L., Clivio, O., Diaz-Ordaz, K., Evans, R. and Holmes, C. (2023) “PWSHAP: a path-wise explanation model for targeted variables”, in Proceedings of the 40th International Conference on Machine Learning. Proceedings of Machine Learning Research, pp. 34054–34089.
Fawkes, J., Evans, R. and Sejdinovic, D. (2022) “Selection, ignorability and challenges with causal fairness”, in Proceedings of the First Conference on Causal Learning and Reasoning. PMLR, pp. 275–289.
Evans, R. (2021) “Dependency in DAG models with hidden variables”, in Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence. Journal of Machine Learning Research, pp. 813–822.

Contact Details

College affiliation: Tutorial Fellow at Jesus College


Office: 1.01

Graduate Students