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

Luís Lourenço (), Luciano Weber, Leandro Garcia, Vinicius Ramos and João Souza
Additional contact information
Luís Lourenço: Department of Knowledge Engineering, Federal University of Santa Catarina, Florianópolis 88035-972, Brazil
Luciano Weber: Department of Knowledge Engineering, Federal University of Santa Catarina, Florianópolis 88035-972, Brazil
Leandro Garcia: Piccolo Mental Health, Florianópolis 88035-400, Brazil
Vinicius Ramos: Department of Knowledge Engineering, Federal University of Santa Catarina, Florianópolis 88035-972, Brazil
João Souza: Department of Knowledge Engineering, Federal University of Santa Catarina, Florianópolis 88035-972, Brazil

IJERPH, 2024, vol. 21, issue 11, 1-12

Abstract: (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.

Keywords: causality; artificial intelligence; health care economics and organizations (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1660-4601/21/11/1484/pdf (application/pdf)
https://www.mdpi.com/1660-4601/21/11/1484/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jijerp:v:21:y:2024:i:11:p:1484-:d:1516264

Access Statistics for this article

IJERPH is currently edited by Ms. Jenna Liu

More articles in IJERPH from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().