Please note: This course will be taught in hybrid mode. Hybrid delivery of courses will include synchronous live sessions during which on campus and online students will be taught simultaneously.
Annalivia Polselli is a British Academy Postdoctoral Fellow at the Institute for Social and Economic Research (ISER), University of Essex. She previously held a postdoctoral position at the Institute for Analytics and Data Science (IADS), University of Essex. She received her Ph.D. in Economics from the University of Essex in 2022. Her research focuses on econometric methods for panel data models, causal machine learning, and applied economics. Her current work advances double machine learning techniques for panel data models.
Her latest publications include:
– Clarke, P. S. and Polselli, A. (2025). Double Machine Learning for Static Panel Models with Fixed Effects. Econometrics Journal. https://doi.org/10.1093/ectj/utaf011
– Leoncini, R., Macaluso, M., and Polselli, A. (2024). Gender segregation: analysis across sectoral dominance in the UK labour market. Empirical Economics. https://doi.org/10.1007/s00181-024-02611-1
She is the author of the R package `xtdml`, available on CRAN (https://cran.r-project.org/web/packages/xtdml/index.html).
Damian is a Postdoctoral Scholar at the Causality in Healthcare AI (CHAI) Hub and a Research Associate in Causal AI at the University of Edinburgh, where his work is centred around improving healthcare AI with causality. He obtained his PhD in Computer Science at the University of Essex and is also a former Software Developer. His main research interests are at the interface of causality and machine learning, with a particular focus on the methods for treatment effect estimation and causal graph learning from observational and temporal data, but also the topics of robustness to data shifts, hyperparameters, and their application in healthcare.
Damian’s latest work:
– Sanchez, P. P., Machlanski, D., McDonagh, S., & Tsaftaris, S. A. (2025). Causal Ordering for Structure Learning From Time Series. arXiv preprint arXiv:2510.24639. To appear in Transactions on Machine Learning Research.
– Machlanski, D., Riley, S., Moroshko, E., Butler, K., Dimitrakopoulos, P., Melistas, T., … & Tsaftaris, S. A. (2025). A shift in perspective on causality in domain generalization. arXiv preprint arXiv:2508.12798.
Course Description
This course offers a comprehensive discussion of various (new and established) machine learning techniques for prediction and causal effect estimation (ATE, ATT, CATE, LATE, HTE) with observational data. The course will cover the use of well-known base learners (e.g., Lasso, decision and boosted trees, random and causal forest, neural networks) for effective causal estimation through meta-learners and doubly/debiased estimation procedures. Note the course puts emphasis on the use of ML base learners in practice rather than explaining their internal design in detail. Best practices in the field will be followed throughout the course, hence the content will also cover how to evaluate obtained models and select among different modelling options.
The course will combine the theory from lectures with practical (hands-on data) sessions in the statistical software R. Practical sessions will use well-established data sets or ad-hoc simulated data to apply the methods presented in the lectures with practical examples.
The main goal of the course is to equip participants with the latest machine learning techniques to conduct data visualisation and causal analysis independently. By the end of the course, the participants will know the challenges that come with observational data and know how to address them through good practice to obtain robust causal estimates.
Prior knowledge of causal estimation is not necessary. Some knowledge of machine learning is recommended but not essential as the topic will be revised at the beginning of the course. However, thorough understanding of statistical modelling is imperative to fully appreciate the course content.
Course Delivery
Note this course is hybrid. Days 1 and 7-10 will we taught in-person by Dr Polselli. Days 2-6 will be taught online by Dr Machlanski. It is possible to attend any days online or in-person. All days (1-10) will be supported by Dr Polselli in person (Colchester) and by Dr Machlanski online (Zoom).
Course Prerequisites
- Working knowledge of R (e.g., data management and visualisation)
- Basics of statistical modelling (OLS, lasso)
- Basics of probability and calculus
Course Objectives
By the end of the course the students will:
- Know the basic principles of causal inference and machine learning.
- Be aware of advantages as well as challenges that come with observational data.
- Understand the role of modelling in causal inference.
- Be comfortable with using various machine learning techniques to estimate causal effects.
- Know how to better understand obtained estimates through visualisation and evaluation metrics.
- Be familiar with the most powerful machine learning methods, including neural networks and generative models, and their use in effect estimation.
- Have an in-depth knowledge of the latest state-of-the-art causal estimators, such as double machine learning.
- Be confident in applying new skills in practical settings.
Suggested Reading (optional)
- James, G., Witten, D., Hastie, T., & Tibshirani, R. (2021). An Introduction to Statistical Learning with Applications in R (Second edition). New York: Springer. ISBN: 978-1-0716-1417-4
- Pearl, J., Glymour, M., & Jewell, N. P. (2016). Causal inference in statistics: A primer. John Wiley & Sons. ISBN: 978-1-119-18684-7
Background knowledge required
Maths:
Calculus – elementary
Linear Regression – elementary
Statistics:
OLS – moderate
Computer background:
R – elementary
