# Leveraging Machine Learning for Official Statistics: A Statistical Manifesto

arXiv: https://arxiv.org/abs/2409.04365 · HTML: https://mlsanity.com/paper/2409.04365/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 42 references that are in the map.
- First posted on arXiv 6 Sep 2024.

## Builds on (most cited first, 3 of 3)

- [Learning from positive and unlabeled data: a survey](https://mlsanity.com/paper/1811.04820/) (2018)
- [Monitoring spatial sustainable development: Semi-automated analysis of satellite and aerial images for energy transition and sustainability indicators](https://mlsanity.com/paper/1810.04881/) (2018)
- [Bias Correction in Machine Learning-based Classification of Rare Events](https://mlsanity.com/paper/2407.06212/) (2024)

## Abstract (arXiv)

It is important for official statistics production to apply ML with statistical rigor, as it presents both opportunities and challenges. Although machine learning has enjoyed rapid technological advances in recent years, its application does not possess the methodological robustness necessary to produce high quality statistical results. In order to account for all sources of error in machine learning models, the Total Machine Learning Error (TMLE) is presented as a framework analogous to the Total Survey Error Model used in survey methodology. As a means of ensuring that ML models are both internally valid as well as externally valid, the TMLE model addresses issues such as representativeness and measurement errors. There are several case studies presented, illustrating the importance of applying more rigor to the application of machine learning in official statistics.