Who benefits from health insurance? Uncovering heterogeneous policy impacts using causal machine learning

Noemi Kreif, Andrew Mirelman, Rodrigo Moreno-Serra (), Taufik Hidayat,, Karla DiazOrdaz and Marc Suhrcke
Additional contact information
Noemi Kreif: Centre for Health Economics, University of York, York, UK
Andrew Mirelman: World Health Organization, Geneva, Switzerland
Taufik Hidayat,: Center for Health Economics and Policy Studies (CHEPS), Faculty of Public Health, Universitas Indonesia, Depok, Indonesia
Karla DiazOrdaz: Department of Medical Statistics, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, UK

No 173cherp, Working Papers from Centre for Health Economics, University of York

Abstract: To be able to target health policies more efficiently, policymakers require knowledge about which individuals benefit most from a particular programme. While traditional approaches for subgroup analyses are constrained only to consider a small number of arbitrarily set, pre-defined subgroups, recently proposed causal machine learning (CML) approaches help explore treatment-effect heterogeneity in a more flexible yet principled way. This paper illustrates one such approach – ‘causal forests’ – in evaluating the effect of mothers’ health insurance enrolment in Indonesia. Contrasting two health insurance schemes (subsidised and contributory) to no insurance, we find beneficial average impacts of enrolment in contributory health insurance on maternal health care utilisation and infant mortality. For subsidised health insurance, however, both effects were smaller and not statistically significant. The causal forest algorithm identified significant heterogeneity in the impacts of the contributory insurance scheme: disadvantaged mothers (i.e. with lower wealth quintiles, lower educated, or in rural areas) benefit the most in terms of increased health care utilisation. No significant heterogeneity was found for the subsidised scheme, even though this programme targeted vulnerable populations. Our study demonstrates the power of CML approaches to uncover the heterogeneity in programme impacts, hence providing policymakers with valuable information for programme design.

Keywords: policy evaluation; machine learning; heterogeneous treatment effects; health insurance (search for similar items in EconPapers)
Pages: 39 pages
Date: 2020-10
New Economics Papers: this item is included in nep-big, nep-dev, nep-ias and nep-sea
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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