Publications

Preprints

• Carey, R., Ansanelli, M. M., Wolfe, E., & Evans, R. J. (2025). Distinguishability of causal structures under latent confounding and selection. arXiv:2509.20433.
• Yang, L., & Evans, R. J. (2025). Outcome-Informed Weighting for Robust ATE Estimation. arXiv:2503.15989.
• Yang, L., Evans, R. J., & Shen, X. (2025). Frugal, Flexible, Faithful: Causal Data Simulation via Frengression. arXiv:2508.01018.
• Lin, X., de Vassimon Manela, D., Mathis, C., Tarp, J. M., & Evans, R. J. (2025). Exact Simulation of Longitudinal Data from Marginal Structural Models. arXiv:2502.07991.
• Lin, X., Tarp, J. M., & Evans, R. J. (2024). Data fusion for efficiency gain in ATE estimation: a practical review with simulations. arXiv:2407.01186.
• Hu, Z., & Evans, R. (2024). A fast score-based search algorithm for maximal ancestral graphs using entropy. arXiv:2402.04777.
• Yao, B., & Evans, R. J. (2022). Regression Identifiability and Edge Interventions in Linear Structural Equation Models. arXiv:2205.13432.
• Armen, A. P., & Evans, R. J. (2018). Towards characterising Bayesian network models under selection. arXiv:1811.05530.
• Hitz, A. S., & Evans, R. J. (2016). Modeling website visits. arXiv:1611.01024.

Published Papers

• Lin, X., Tarp, J. M., & Evans, R. J. (2025). Combining experimental and observational data through a power likelihood. Biometrics, 81(1), ujaf008.
• de Vassimon Manela, D., Yang, L., & Evans, R. J. (2025). Testing Generalizability in Causal Inference. Proceedings of the 41st Conference on Uncertainty in Artificial Intelligence.
• Evans, R. J., & Didelez, V. (2024). Parameterizing and simulating from causal models. Journal of the Royal Statistical Society Series B: Statistical Methodology, 86(3), 535–568.
• de Vassimon Manela, D., Battaglia, L., & Evans, R. (2024). Marginal causal flows for validation and inference. Advances in Neural Information Processing Systems, 37, 9920–9949.
• Hu, Z., & Evans, R. J. (2024). Towards standard imsets for maximal ancestral graphs. Bernoulli, 30(3), 2026–2051.
• Hu, R., Sejdinovic, D., & Evans, R. J. (2024). A kernel test for causal association via noise contrastive backdoor adjustment. Journal of Machine Learning Research, 25(160), 1–56.
• Carey, R., Lee, S., & Evans, R. J. (2024). Toward a complete criterion for value of information in insoluble decision problems. Transactions of Machine Learning Research.
• Tamambang, R., Kusi-Mensah, K., Bella-Awusah, T., Ogunmola, O., Afolayan, A., Toska, E., Hertzog, L., Rudgard, W., Evans, R., Stöeckl, H., & et al. (2024). Two are Better Than One but Three is Best: Fast-Tracking the Attainment of the Sustainable Development Goals (SDGs) Among In-School Adolescents in Nigeria. Child Indicators Research, 17(5), 2219–2241.
• Tamambang, R., Kusi-Mensah, K., Bella-Awusah, T., Ogunmola, O., Afolayan, A., Toska, E., Hertzog, L., Rudgard, W., Evans, R., & Omigbodun, O. (2024). Identifying potential catalysts to accelerate the achievement of Sustainable Development Goals (SDGs) among adolescents living in Nigeria. Psychology, Health & Medicine, 29(4), 868–887.
• Richardson, T. S., Evans, R. J., Robins, J. M., & Shpitser, I. (2023). Nested Markov properties for acyclic directed mixed graphs. The Annals of Statistics, 51(1), 334–361.
• Evans, R. J. (2023). Latent free equivalent mDAGs. Algebraic Statistics, 14(1), 3–16.
• Fawkes, J., & Evans, R. J. (2023). Results on counterfactual invariance. SCIS Workshop, ICML.
• Fawkes, J., Hu, R., Evans, R. J., & Sejdinovic, D. (2023). Doubly robust kernel statistics for testing distributional treatment effects even under one sided overlap. Transactions of Machine Learning Research.
• Ter-Minassian, L., Clivio, O., Diazordaz, K., Evans, R. J., & Holmes, C. C. (2023). PWSHAP: a path-wise explanation model for targeted variables. International Conference on Machine Learning, 34054–34089.
• Yao, B., & Evans, R. (2022). Algebraic properties of HTC-identifiable graphs. Algebraic Statistics, 13(1), 19–39.
• Kusi-Mensah, K., Tamambang, R., Bella-Awusah, T., Ogunmola, S., Afolayan, A., Toska, E., Hertzog, L., Rudgard, W., Evans, R., & Omigbodun, O. (2022). Accelerating progress towards the sustainable development goals for adolescents in Ghana: a cross-sectional study. Psychology, Health & Medicine, 27(sup1), 49–66.
• Hinze, V., Ford, T., Evans, R., Gjelsvik, B., & Crane, C. (2022). Exploring the relationship between pain and self-harm thoughts and behaviours in young people using network analysis. Psychological Medicine, 52(15), 3560–3569.
• Fawkes, J., Evans, R., & Sejdinovic, D. (2022). Selection, ignorability and challenges with causal fairness. Conference on Causal Learning and Reasoning, 275–289.
• Evans, R. J. (2021). Dependency in DAG models with hidden variables. Proceedings of the 37th Conference on Uncertainty in Artificial Intelligence, 813–822.
• Černis, E., Evans, R., Ehlers, A., & Freeman, D. (2021). Dissociation in relation to other mental health conditions: An exploration using network analysis. Journal of Psychiatric Research, 136, 460–467.
• Hu, Z., & Evans, R. (2020). Faster algorithms for Markov equivalence. Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence, 739–748.
• Evans, R. J. (2020). Model selection and local geometry. The Annals of Statistics, 48(6), 3513–3544.
• Evans, R. J., & Richardson, T. S. (2019). Smooth, identifiable supermodels of discrete DAG models with latent variables. Bernoulli, 848–876.
• Bird, J. C., Evans, R., Waite, F., Loe, B. S., & Freeman, D. (2019). Adolescent paranoia: Prevalence, structure, and causal mechanisms. Schizophrenia Bulletin, 45(5), 1134–1142.
• Allman, E. S., Baños, H., Evans, R., Hoşten, S., Kubjas, K., Lemke, D., Rhodes, J. A., & Zwiernik, P. (2019). Maximum Likelihood Estimation of the Latent Class Model through Model Boundary Decomposition. Journal of Algebraic Statistics, 10(1), 51–84.
• Shpitser, I., Evans, R. J., & Richardson, T. S. (2018). Acyclic linear SEMs obey the nested Markov property. Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, 255.
• Evans, R. (2018). Markov properties for mixed graphical models. Handbook of Graphical Models, 39–60.
• Evans, R. J. (2018). Margins of discrete Bayesian networks. The Annals of Statistics, 46(6A), 2623–2656.
• Didelez, V., & Evans, R. J. (2018). Causal inference from case-control studies. Handbook of Statistical Methods for Case-Control Studies, 87–115.
• Nowzohour, C., Maathuis, M. H., Evans, R. J., & Bühlmann, P. (2017). Distributional equivalence and structure learning for bow-free acyclic path diagrams. Electronic Journal of Statistics.
• Silva, R., & Evans, R. (2016). Causal inference through a witness protection program. Journal of Machine Learning Research, 17(56), 1–53.
• Hitz, A., & Evans, R. (2016). One-component regular variation and graphical modeling of extremes. Journal of Applied Probability, 53(3), 733–746.
• Evans, R. J. (2016). Graphs for margins of Bayesian networks. Scandinavian Journal of Statistics, 43(3), 625–648.
• Evans, R. J. (2015). Smoothness of marginal log-linear parameterizations. Electronic Journal of Statistics, 9, 475–491.
• Evans, R. J., & Didelez, V. (2015). Recovering from Selection Bias using Marginal Structure in Discrete Models. ACI @ UAI, 46–55.
• Silva, R., & Evans, R. (2014). Causal inference through a witness protection program. Advances in Neural Information Processing Systems, 27.
• Shpitser, I., Evans, R. J., Richardson, T. S., & Robins, J. M. (2014). Introduction to nested Markov models. Behaviormetrika, 41(1), 3–39.
• Evans, R. J., & Richardson, T. S. (2014). Markovian acyclic directed mixed graphs for discrete data. Annals of Statistics, 1452–1482.
• Evans, R. J. (2014). Graphical latent structure testing. Advances in Latent Variables: Methods, Models and Applications, 253–262.
• Shpitser, I., Evans, R. J., Richardson, T. S., & Robins, J. M. (2013). Sparse nested Markov models with log-linear parameters. Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence.
• Evans, R. J., & Forcina, A. (2013). Two algorithms for fitting constrained marginal models. Computational Statistics & Data Analysis, 66, 1–7.
• Evans, R. J., & Richardson, T. S. (2013). Marginal log-linear parameters for graphical Markov models. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 75(4), 743–768.
• Evans, R. J. (2013). Comment: Acyclic Directed Mixed Graphs for Marginalized Models. Sociological Methodology, 43(1), 104–106.
• Shpitser, I., Richardson, T. S., Robins, J. M., & Evans, R. J. (2012). Parameter and structure learning in nested Markov models. 28th Conference on Uncertainty in Artificial Intelligence (Causal Structure Learning Workshop).
• Evans, R. J. (2012). Graphical methods for inequality constraints in marginalized DAGs. 2012 IEEE International Workshop on Machine Learning for Signal Processing, 1–6.
• Richardson, T. S., Evans, R. J., & Robins, J. M. (2011). Transparent parameterizations of models for potential outcomes. Bayesian Statistics, 9, 569–610.
• Evans, R. J. (2011). Parametrizations of discrete graphical models.
• Evans, R. J. (2011). Lectures on Algebraic Statistics. SIAM Review, 53(1), 189.
• Evans, R. J., & Richardson, T. S. (2010). Maximum likelihood fitting of acyclic directed mixed graphs to binary data. Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence.
• Evans, R. (2007). Rates of Convergence of Maximum Likelihood Estimators Via Entropy Methods.