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Activities

August 2020 - Discussion of Varando and Hansen's Graphical continuous Lyapunov models, UAI 2020

June 2020 - Seminar at MRC Biostatistics Unit, Cambridge

June 2020 - Seminar at Karolinska Institute, Stockholm

April 2020 - Workshop at Villa Garbald, Switzerland

March 2020 - AIM SQuaRE on Nested Models, San Jose, California

October 2019 - Graphical Models: Conditional Independence and Algebraic Structures, TU Munich

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Preprints

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 Constraints in Gaussian Graphical Models
(with Bohao Yao)

 

Publications

2020 a Model selection and local geometry
Annals of Statistics (to appear)
b Faster Algorithms for Markov equivalence
(with Zhongyi Hu) UAI-20
c Dissociation in relation to other mental health conditions: An exploration using network analysis
(with Emma ńĆernis, Anke Ehlers and Daniel Freeman) Journal of Psychiatric Research
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)

 

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

 

Talks

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