# A step towards the integration of machine learning and classic model-based survey methods

arXiv: https://arxiv.org/abs/2402.07521 · HTML: https://mlsanity.com/paper/2402.07521/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 67 references that are in the map.
- First posted on arXiv 12 Feb 2024.

## Builds on (most cited first, 7 of 7)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Kaggle forecasting competitions: An overlooked learning opportunity](https://mlsanity.com/paper/2009.07701/) (2020)
- [Interpretable Poverty Mapping using Social Media Data, Satellite Images, and Geospatial Information](https://mlsanity.com/paper/2011.13563/) (2020)
- [Improving the output quality of official statistics based on machine learning algorithms](https://mlsanity.com/paper/2103.00834/) (2021)
- [Model-assisted estimation through random forests in finite population sampling](https://mlsanity.com/paper/2002.09736/) (2020)
- [New Important Developments in Small Area Estimation](https://mlsanity.com/paper/1302.4907/) (2013)
- [Flexible domain prediction using mixed effects random forests](https://mlsanity.com/paper/2201.10933/) (2022)

## Built on by (most cited first, 1 of 1)

- [Geographically Weighted Surrogate Models for Rapid Small-Area Chronic Disease Estimation](https://mlsanity.com/paper/2607.28655/) (2026)

## Abstract (arXiv)

The usage of machine learning methods in traditional surveys including official statistics, is still very limited. Therefore, we propose a predictor supported by these algorithms, which can be used to predict any population or subpopulation characteristics. Machine learning methods have already been shown to be very powerful in identifying and modelling complex and nonlinear relationships between the variables, which means they have very good properties in case of strong departures from the classic assumptions. Therefore, we analyse the performance of our proposal under a different set-up, which, in our opinion, is of greater importance in real-life surveys. We study only small departures from the assumed model to show that our proposal is a good alternative, even in comparison with optimal methods under the model. Moreover, we propose the method of the ex ante accuracy estimation of machine learning predictors, giving the possibility of the accuracy comparison with classic methods. The solution to this problem is indicated in the literature as one of the key issues in integrating these approaches. The simulation studies are based on a real, longitudinal dataset, where the prediction of subpopulation characteristics is considered.