Robin Evans

a	Modeling Website Visits (with Adrien Hitz)
b	Nested Markov Properties for Acyclic Directed Mixed Graphs (with Thomas Richardson, James Robins and Ilya Shpitser)
c	Towards Characterising Bayesian Network Models under Selection (with Angelos Armen)
d	Parameterizing and Simulating from Causal Models (with Vanessa Didelez)
e	A Kernel Test for Causal Association via Noise Contrastive Backdoor Adjustment (with Robert Hu and Dino Sejdinovic)
f	Regression Identifiability and Edge Interventions in Linear Structural Equation Models (with Bohao Yao)

Publications

2022	a	Selection, Ignorability and Challenges With Causal Fairness (with Jake Fawkes and Dino Sejdinovic) CLeaR 2022
	b	Algebraic Properties of Gaussian HTC-identifiable Graphs (with Bohao Yao) Algebraic Statistics (accepted)
	c	Accelerating Progress Towards the Sustainable Development Goals for adolescents in Ghana: a cross-sectional study (with Kwabena Kusi-Mensah and others) Psychology, Health & Medicine (accepted)
2021	a	Dependency in DAG models with hidden variables UAI-21, PMLR 161 pp 813-822
	b	Exploring the relationship between pain and self-harm thoughts and behaviours in young people using network analysis (with Verena Hinze, Tamsin Ford, Bergljot Gjelsvik and Catherine Crane) Psychological Medicine, 1-10
2020	a	Model selection and local geometry Annals of Statistics, 48 (6), pp 3514-3544
	b	Faster Algorithms for Markov equivalence (with Zhongyi Hu) UAI-20, PMLR 124 pp 739-748
	c	Dissociation in relation to other mental health conditions: An exploration using network analysis (with Emma Černis, Anke Ehlers and Daniel Freeman) Journal of Psychiatric Research
	d	Comment on: Graphical models for extremes by Engelke and Hitz Journal of the Royal Statistical Society, Series B, 82 (4), pp 919-920
2019	a	Smooth, identifiable supermodels of discrete DAG models with latent variables (with Thomas Richardson) Bernoulli, 25 (2) pp 848-876
	b	Maximum likelihood estimation of the Latent Class Model through model boundary decomposition (with Elizabeth Allman and others) Journal of Algebraic Statistics, 10 (1) pp 51-84
	c	Adolescent Paranoia: Prevalence, Structure, and Causal Mechanisms (with Jessica Bird and others), Schizophrenia Bulletin, 45 (5), pp 1134-1142
	d	Markov Properties for Mixed Graphical Models Chapter 2 of Handbook of Graphical Models (Maathuis et al., Eds)
2018	a	Margins of discrete Bayesian networks Annals of Statistics, 46 (6A) pp 2623-2656
	b	Acyclic Linear SEMs Obey the Nested Markov Property (with Ilya Shpitser and Thomas Richardson) UAI-18, (supplementary material)
	c	Causal Inference from Case-Control Studies (with Vanessa Didelez) Chapter 6 of Handbook of Statistical Methods for Case-Control Studies (Borgan et al., Eds)
2017		Distributional equivalence and structure Learning for Bow-free Acyclic Path Diagrams (with Christopher Nowzohour, Marloes H. Maathuis and Peter Bühlmann) Electronic Journal of Statistics, 11 (2), pp 5342-5374
2016	a	Graphs for margins of Bayesian networks Scandinavian Journal of Statistics, 43 (3), pp 625-648
	b	Causal Inference through a Witness Protection Program (with Ricardo Silva) Journal of Machine Learning Research 17 (56) pp 1-53 (expansion of NIPS paper below)
	c	One-Component Regular Variation and Graphical Modeling of Extremes (with Adrien Hitz) Journal of Applied Probability, 53 (3), pp 733-746
2015	a	Smoothness of marginal log-linear parameterizations Electronic Journal of Statistics, 9 (1), pp 475-491
	b	Recovering from Selection Bias using Marginal Structure in Discrete Models (with Vanessa Didelez), UAI-15, Advances in Causal Inference Workshop.
2014	a	Markovian acyclic directed mixed graphs for discrete data (with Thomas Richardson), Annals of Statistics, 42 (4), pp 1452-1482
	b	Causal Inference through a Witness Protection Program (with Ricardo Silva) NIPS 27
	c	Introduction to nested Markov models (with Ilya Shpitser, Thomas Richardson and James Robins) Behaviormetrika 41 (1) pp 3-39
	d	Graphical latent structure testing Studies in Theoretical and Applied Statistics, Springer
2013	a	Marginal log-linear parameters for graphical Markov models (with Thomas Richardson), J. Roy. Statist. Soc. B, 75 (4) pp 743-768 (software for simulations and data analysis available here)
	b	Two algorithms for fitting constrained marginal models (with Antonio Forcina), Computational Statistics and Data Analysis, 66 pp 1-7.
	c	Sparse nested Markov models with log-linear parameters (with Ilya Shpitser, Thomas Richardson and James Robins) UAI-13, pp 576-585
	d	Comment on: On the application of discrete marginal graphical models, by Németh and Rudas Sociological Methodology, 43 (1) pp 105-107
2012	a	Graphical methods for inequality constraints in marginalized DAGs 22nd Workshop on Machine Learning and Signal Processing
	b	Parameter and Structure Learning in Nested Markov Models (with Ilya Shpitser, Thomas Richardson and James Robins) UAI-12, Causal Structure Learning Workshop.
2011		Transparent parametrizations of models for potential outcomes (with discussion) (with Thomas Richardson and James Robins), Bayesian Statistics 9, pp 569-610
2010		Maximum likelihood fitting of acyclic directed mixed graphs to binary data (with Thomas Richardson), UAI-10, pp 177-184

Thesis

Parametrizations of Discrete Graphical Models, University of Washington, 2011.
Supervisor: Thomas Richardson. (this version includes some minor corrections from the original)

Talks

mDAGs Equivalent to Latent-Free DAGs, Quantum Workshop, Simons Institute

The Inflation Technique, Algebraic Aspects of Causality Reading Group, Simons Program on Causality

Parameterizing and Simulating from Causal Models, Talk at Pacific Causal Inference Conference, September 2021

Faster models for Markov equvialence, Talk at ISI, July 2021

Parameterizing Causal Models, Karolinska and MRC Cambridge Seminars, June 2020

Angles and Model Selection, Technische Universität München, October 2019

Model Selection and Local Geometry - Workshop on Causal inference for complex graphical structures, Montreal, June 2018

Causal Models with Latent Variables - Quantum Networks Workshop, Oxford, August 2017

Geometry of Graphical Model Selection - ICMS, April 2017

Marginal and Causal models - LSHTM, June 2016

Causal models and how to refute them - University of York, November 2015

UAI Tutorial on Causal Models (with video), July 2015

Graphs for margins of Bayesian networks - ERCIM, Pisa, December 2014

Equality constraints on Marginalised DAGs and their uses - Algebraic Statistics Workshop, Daejeon, July 2014

Inequality constraints on Marginalised DAGs - UK CIM, Manchester, May 2013

Marginal log-linear parameters, graphical models and model selection - Statistics Seminar, University of Bristol, January 2012

Variation Independent Parametrizations (with video) - CSI One Day Meeting, September 2011

Parametrizations of Discrete Graphical Models - UW Final Examination, August 2011

Smoothness of Binary Conditional Independence Models - WOGAS3, April 2011

Probabilistic Causal Models: A Short Introduction - ACMS Seminar, February 2011

Parametrizations of Discrete Graphical Models - UW General Examination, December 2010

Factor Analysis and Singularities - Slides from presentation for STAT 591, October 2009

Other Work

(not to be reproduced without appropriate citation / permission)

2009	Discussion of Generalized Additive Models with Implicit Variable Selection by Likelihood-Based Boosting, G. Tutz and H. Binder STAT 572 Project; a version of Tutz and Binder's paper can be found here, final version published in Biometrics 62 (2006)
2009	Maximum Likelihood Estimates for Binary Random Variables on Trees via Phylogenetic Ideals A project for STAT 538 - a version of Zwiernik and Smith's paper can be found here
2007	Rates of Convergence of Non-Parametric Maximum Likelihood Estimators via Entropy Methods

Department of Statistics