Leveraging Machine Learning for Official Statistics: A Statistical Manifesto
This addresses the problem of producing high-quality, reliable official statistics for government and policy-making by introducing a more rigorous statistical framework, though it is incremental as it adapts existing survey methodology concepts to machine learning.
The paper tackles the lack of methodological robustness in applying machine learning to official statistics by proposing the Total Machine Learning Error (TMLE) framework, analogous to Total Survey Error, to account for all error sources and ensure validity, with case studies illustrating its importance.
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.