Robin Evans' Research Group

Find more details on Google Scholar.

Preprints

Linying Yang and Robin J. Evans (2025). Outcome-Informed Weighting for Robust ATE Estimation
Xi Lin, Daniel de Vassimon Manela, Chase Mathis, Jens Magelund Tarp, Robin J. Evans (2025). Simulating Longitudinal Data from Marginal Structural Models.
Xi Lin, Jens Magelund Tarp and Robin J. Evans (2024). Data fusion for efficiency gain in ATE estimation: A practical review with simulations.
Zhongyi Hu and Robin J. Evans (2024). A fast score-based search algorithm for maximal ancestral graphs using entropy.
Bohao Yao and Robin J. Evans (2023). Regression Identifiability and Edge Interventions in Linear Structural Equation Models.

2025

Xi Lin, Jens Magelund Tarp and Robin J. Evans. Combining Experimental and Observational Data through a Power Likelihood

Biometrics 81 (1) ujaf008.

Daniel de Vassimon Manela, Linying Yang and Robin J. Evans (2025). Testing Generalizability in Causal Inference, UAI-25.

2024

Robin J. Evans and Vanessa Didelez. Parameterizing and Simulating from Causal Models (with discussion).
Journal of the Royal Statistical Society, Series B 86 (3), pp 535–568.
Zhongyi Hu and Robin J. Evans. Towards standard imsets for maximal ancestral graphs. Bernoulli 30 (3), pp 2026-2051.
Daniel de Vassimon Manela, Laura Battaglia and Robin J. Evans. Marginal Causal Flows for Validation and Inference. NeurIPS.
Robert Hu and Dino Sejdinovic and Robin J. Evans. A Kernel Test for Causal Association via Noise Contrastive Backdoor Adjustment.
Journal of Machine Learning Research 25 (160), pp 1–56.
Jake Fawkes, Robert Hu, Dino Sejdinovic and Robin J. Evans. Doubly Robust Kernel Statistics for Testing Distributional Treatment Effects Even Under One Sided Overlap.
Transactions of Machine Learning Research.
Ryan Carey, Sanghack Lee and Robin J. Evans. Toward a Complete Criterion for Value of Information in Insoluble Decision Problems.
Transactions of Machine Learning Research.
Rita Tamambang et al. Identifying potential catalysts to accelerate the achievement of SDGs among adolescents living in Nigeria
Psychology, Health & Medicine 29 (4) pp 868–887
Rita Tamambang et al. Two are Better Than One but Three is Best
Child Indicators Research 17 pp 2219–2241

2023

Thomas Richardson, Robin J. Evans, James Robins and Ilya Shpitser. Nested Markov Properties for Acyclic Directed Mixed Graphs.
Annals of Statistics 51 (1) pp 334–361.
Robin J. Evans. Latent-free equivalent mDAGs. Algebraic Statistics 14 (1) pp 3–16.
Lucile Ter-Minassian, Oscar Clivio, Karla Diaz-Ordaz, Robin J. Evans and Chris Holmes. PWSHAP: A Path-Wise Explanation Model for Targeted Variables.
ICML, PMLR 202: pp 34054–34089.
Jake Fawkes and Robin J. Evans (2023). Results on Counterfactual Invariance.
SCIS (ICML Workshop).

2022

Jake Fawkes, Robin J. Evans and Dino Sejdinovic. Selection, Ignorability and Challenges With Causal Fairness. CLeaR 2022.
Bohao Yao and Robin J. Evans. Algebraic Properties of Gaussian HTC-identifiable Graphs. Algebraic Statistics 13 (1) pp 19–39.
Kwabena Kusi-Mensah et al. Accelerating Progress Towards the Sustainable Development Goals for adolescents in Ghana: a cross-sectional study
Psychology, Health & Medicine 27 pp 49–66.

2021

Robin J. Evans. Dependency in DAG models with hidden variables, UAI-21, PMLR 161 pp 813-822
Verena Hinze, Tamsin Ford, Bergljot Gjelsvik, Robin J. Evans and Catherine Crane. Exploring the relationship between pain and self-harm thoughts and behaviours in young people using network analysis, Psychological Medicine, 1-10

2020

Robin J. Evans. Model selection and local geometry. Annals of Statistics, 48 (6), pp 3514-3544
Zhongyi Hu and Robin J. Evans. Faster Algorithms for Markov equivalence. UAI-20, PMLR 124 pp 739-748
Emma Černis, Anke Ehlers, Robin J. Evans and Daniel Freeman. Dissociation in relation to other mental health conditions: An exploration using network analysis.
Journal of Psychiatric Research
Robin J. Evans. Comment on: Graphical models for extremes by Engelke and Hitz.
Journal of the Royal Statistical Society, Series B, 82 (4), pp 919-920

2019

Robin J. Evans and Thomas S. Richardson. Smooth, identifiable supermodels of discrete DAG models with latent variables
Bernoulli, 25 (2) pp 848-876
Elizabeth Allman et al. Maximum likelihood estimation of the Latent Class Model through model boundary decomposition
Journal of Algebraic Statistics, 10 (1) pp 51-84
Jessica Bird, ... Adolescent Paranoia: Prevalence, Structure, and Causal Mechanisms
Schizophrenia Bulletin, 45 (5), pp 1134-1142
Robin J. Evans. Markov Properties for Mixed Graphical Models
Chapter 2 of Handbook of Graphical Models (Maathuis et al., Eds)

2018

Robin J. Evans. Margins of discrete Bayesian networks
Annals of Statistics, 46 (6A) pp 2623-2656
Ilya Shpitser, Robin J. Evans and Thomas Richardson. Acyclic Linear SEMs Obey the Nested Markov Property
UAI-18, (supplementary material)
Vanessa Didelez and Robin J. Evans. Causal Inference from Case-Control Studies
Chapter 6 of Handbook of Statistical Methods for Case-Control Studies (Borgan et al., Eds)

2017 and earlier

Christopher Nowzohour, Marloes H. Maathuis, Robin J. Evans and Peter Bühlmann. Distributional equivalence and structure Learning for Bow-free Acyclic Path Diagrams
Electronic Journal of Statistics, 11 (2), pp 5342-5374

2016

Robin J. Evans. Graphs for margins of Bayesian networks
Scandinavian Journal of Statistics, 43 (3), pp 625-648
Ricardo Silva and Robin J. Evans. Causal Inference through a Witness Protection Program

Journal of Machine Learning Research 17 (56) pp 1-53 (expansion of 2014 NIPS paper)

Adrien Hitz and Robin J. Evans. One-Component Regular Variation and Graphical Modeling of Extremes

Journal of Applied Probability, 53 (3), pp 733-746

2015

Robin J. Evans. Smoothness of marginal log-linear parameterizations
Electronic Journal of Statistics, 9 (1), pp 475-491
Robin J. Evans. and Vanessa Didelez. Recovering from Selection Bias using Marginal Structure in Discrete Models

(with), UAI-15, Advances in Causal Inference Workshop.

2014

Robin J. Evans. and Thomas S. Richardson. Markovian acyclic directed mixed graphs for discrete data

Annals of Statistics, 42 (4), pp 1452-1482

Ricardo Silva and Robin J. Evans. Causal Inference through a Witness Protection Program
NIPS 27
Ilya Shpitser, Robin J. Evans, Thomas Richardson and James Robins. Introduction to nested Markov models
Behaviormetrika 41 (1) pp 3-39
Robin J. Evans. Graphical latent structure testing
Studies in Theoretical and Applied Statistics, Springer

2013

Robin J. Evans. and Thomas S. Richardson. Marginal log-linear parameters for graphical Markov models

J. Roy. Statist. Soc. B, 75 (4) pp 743-768

(software for simulations and data analysis available here)

Robin J. Evans and Antonio Forcina. Two algorithms for fitting constrained marginal models

Computational Statistics and Data Analysis, 66 pp 1-7.

Ilya Shpitser, Robin J. Evans, Thomas Richardson and James Robins. Sparse nested Markov models with log-linear parameters

UAI-13, pp 576-585

Robin J. Evans. Comment on: On the application of discrete marginal graphical models, by Németh and Rudas

Sociological Methodology, 43 (1) pp 105-107

2012

Robin J. Evans. Graphical methods for inequality constraints in marginalized DAGs

22nd Workshop on Machine Learning and Signal Processing

Ilya Shpitser, Thomas Richardson, James Robins and Robin J. Evans. Parameter and Structure Learning in Nested Markov Models

UAI-12, Causal Structure Learning Workshop.

2011

Thomas S. Richardson, Robin J. Evans. and James M. Robins. Transparent parametrizations of models for potential outcomes (with discussion)

Bayesian Statistics 9, pp 569-610

2010

Robin J. Evans. and Thomas S. Richardson. Maximum likelihood fitting of acyclic directed mixed graphs to binary data

UAI-10, pp 177-184

Check Robin's Google Scholar for the full list!

Page updated

Google Sites

Report abuse