Leveraging Machine Learning for Official Statistics: A Statistical Manifesto

TL;DR

This paper advocates for integrating machine learning into official statistics with rigorous statistical methods, introducing the Total Machine Learning Error framework to ensure validity and reliability.

Contribution

It introduces the TMLE framework to address errors in ML applications for official statistics, emphasizing methodological rigor and validation.

Findings

TMLE framework parallels Total Survey Error Model

Case studies demonstrate need for rigorous ML application

Highlights challenges and solutions in ML for official stats

Abstract

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.