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

Research output: Working paper



Publication details

DatePublished - 6 Oct 2020
PublisherCentre for Health Economics, University of York
Place of PublicationYork, UK
Number of pages39
Original languageEnglish

Publication series

NameCHE Research Paper
PublisherCentre for Health Economics, University of York


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.

    Research areas

  • policy evaluation, machine learning, heterogeneous treatment effects, health insurance

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