Machine Learning Algorithms to Estimate Propensity Scores in Health Policy Evaluation: A Scoping Review

(1) Background: Quasi-experimental design has been widely used in causal inference for health policy impact evaluation. However, due to the non-randomized treatment used, there is great potential for bias in the assessment of the results, which can be reduced by using propensity score (PS) methods. In this context, this article aims to map the literature concerning the use of machine learning (ML) algorithms for propensity score estimation. (2) Methods: A scoping review was carried out in the PubMed, EMBASE, ACM Digital Library, IEEE Explore, LILACS, Web of Science, Scopus, Compendex, and gray literature (ProQuest and Google Scholar) databases, based on the PRISMA-ScR guidelines. This scoping review aims to identify ML models and their accuracy and the characteristics of studies on causal inference for health policy impacts, with a specific focus on PS estimation using ML. (3) Results: Seven studies were included in the review from 3018 references searched. In general, tree-based ML models were used for PS estimation. Most of the studies did not show or mention the performance metrics of the selected models, focusing instead on discussing the treatment effects under analysis. (4) Conclusions: Despite important aspects of model development and evaluation being under-reported, this scoping review provides insights into the recent use of ML algorithms in health policy impact evaluation.