Studies from University of Washington in the Area of Machine Learning Reported (Estimating heterogeneous impacts Of subsidised health insurance: A causal machine learning approach): Machine Learning

2025 OCT 09 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- Researchers detail new data in Machine Learning. According to news reporting out of Seattle, Washington, by NewsRx editors, research stated, “The evaluation of social and health policies often necessitates understanding the variations in impacts based on recipients’ observed characteristics, underscoring the value of estimating treatment effect heterogeneity. In this study, we leverage predictive and causal machine learning to assess the impact of the subsidised component of Indonesia’s National Health Insurance Programme (‘JKN’) on healthcare utilisation in 2017.”

Financial support for this research came from Medical Research Council.

Our news journalists obtained a quote from the research from the University of Washington, “We employ causal forests for estimating heterogeneous treatment effects and the super learner algorithm for prediction tasks. Our approach addresses the prevalence of zeros in the utilisation outcomes through a two-part model, which separates the outcome model into zero and non-zero counts. This allows for distinct investigation of policy impacts on the decision to seek care and the quantity of care consumed. We interpret and summarise treatment effect heterogeneity using various approaches, including data-driven subgroup analyses and linear projections, which are grounded in theory. Our results demonstrate a positive average impact on healthcare demand with evident heterogeneity; for instance, the increase in demand varies among recipients.”

According to the news editors, the research concluded: “We also find that the effect is modified by a set of theoretically motivated covariates and those identified through our data-driven approach.”

For more information on this research see: Estimating heterogeneous impacts Of subsidised health insurance: A causal machine learning approach. PLOS One, 2025;20(9). PLOS One can be contacted at: Public Library Science, 1160 Battery Street, Ste 100, San Francisco, CA 94111, USA. (Public Library of Science - www.plos.org; PLOS One - www.plosone.org)

Our news journalists report that additional information may be obtained by contacting Noemi Kreif, CHOICE Institute, School of Pharmacy, University of Washington, Seattle, Washington, United States.

The direct object identifier (DOI) for that additional information is: https://doi.org/10.1371/journal.pone.0315057. This DOI is a link to an online electronic document that is either free or for purchase, and can be your direct source for a journal article and its citation.

Publisher contact information for the journal PLOS One is: Public Library Science, 1160 Battery Street, Ste 100, San Francisco, CA 94111, USA.

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