August 2017 - Quantum Networks Workshop, Oxford

August 2016 - Joint Statistical Meetings, Chicago

July 2016 - Workshop on Statistical Causal Inference and its Applications to Genetics, CRM, Montreal

June 2016 - Seminar, London School of Hygiene and Tropical Medicine

April 2016 - Statistics Seminar, University of Bristol

April 2016 - UK Causal Inference Meeting, London (my slides)

January 2016 - Statistics Seminar, University of Kent

November 2015 - Statistics Seminar, University of York

October 2015 - Statistics Seminar, University of Bath




a Smooth, identifiable supermodels of discrete DAG models with latent variables
(with Thomas Richardson)
b Modeling Website Visits
(with Adrien Hitz)
c Nested Markov Properties for Acyclic Directed Mixed Graphs
(with Thomas Richardson, James Robins and Ilya Shpitser)
d Structure Learning with Bow-free Acyclic Path Diagrams
(with Christopher Nowzohour, Marloes H. Maathuis and Peter Bühlmann)
e Maximum likelihood estimation of the Latent Class Model through model boundary decomposition
(with Elizabeth Allman and others)



2017 Margins of discrete Bayesian networks
Accepted to Annals of Statistics
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
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



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


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



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