Dr Annalivia Polselli BA Postdoctoral Fellow, University of Essex

Annalivia Polselli
Email
ap17181@essex.ac.uk
Office
2N2.6.13B
Curriculum vitae
I am a British Academy Postdoctoral Fellow at the Institute for Social and Economic Research (ISER). My research lies at the intersection of econometric theory and applied economics, with a focus on causal inference and machine learning methods for panel data. My current work develops double machine learning methods for panel data models across a range of settings, including homogeneous and heterogeneous treatment effects, interactive fixed effects, and instrumental variables.
I received my Ph.D. in Economics from the University of Essex in 2022, and prior to my current position I held a postdoctoral fellowship at the Institute for Analytics and Data Science (IADS), also at Essex.
I am the author of the R package `xtdml` which is freely available on CRAN. Other community-contributed R and Stata packages can be found on my GitHub repo: https://github.com/POLSEAN. Please visit my personal website for updates: https://sites.google.com/site/annaliviapolselli/home
Latest Publications
  1. Clarke, P. S. and Polselli, A. (2025). Double Machine Learning for Static Panel Models with Fixed Effects. Econometrics Journal. 29(1), 69–86, https://doi.org/10.1093/ectj/utaf011. (Editor’s choice)
  2. 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 

Working Papers

  1. Double Machine Learning for Static Panel Models with Instrumental variables: Method and Applications (with A. Baiardi, P. S. Clarke and A. Naghi), 2026. https://arxiv.org/abs/2603.20464
  2. xtdml: Double Machine Learning Estimation to Static Panel Data Models with Fixed Effects in R, 2025. https://arxiv.org/abs/2512.15965.
  3. Influence Analysis with Panel Data, 2023. https://arxiv.org/pdf/2312.05700.
  4. Robust Statistical Inference in Panel Data Models with Fixed Effects, 2023. https://arxiv.org/abs/2312.17676 .