Estimating heterogeneous policy impacts using causal machine learning: a case study of health insurance reform in Indonesia
Estimating heterogeneous policy impacts using causal machine learning: a case study of health insurance reform in Indonesia
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DOI：
10.1007/s10742-021-00259-3
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发表时间：
2021-11
影响因子：
1.5
通讯作者：
N. Kreif;K. DiazOrdaz;R. Moreno-Serra;A. Mirelman;Taufik Hidayat-;M. Suhrcke
中科院分区：
文献类型：
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作者：
N. Kreif;K. DiazOrdaz;R. Moreno-Serra;A. Mirelman;Taufik Hidayat-;M. Suhrcke
寻求有效针对特定人口群体的卫生政策的决策者需要知道哪些人将从每项政策中受益最多。虽然传统的亚组分析方法仅限于考虑少量预定义的亚组，但最近提出的因果机器学习（CML）方法有助于以更灵活但更有原则的方式探索治疗效果异质性。因果森林使用随机森林算法的推广来估计个体和亚组水平的异质性治疗效应。我们的论文旨在探讨这种方法在卫生政策评估的设置与强大的观察到的混杂，特别适用于在印度尼西亚的母亲的健康保险登记的背景下。比较两个健康保险计划（补贴和缴费）对没有保险，我们发现有益的平均影响，在缴费健康保险的孕产妇保健利用率和婴儿死亡率的入学率，但没有影响的补贴健康保险。因果森林算法确定了缴费保险影响的显著异质性，不仅沿着我们预先指定的社会经济变量（表明贫困，教育程度较低和农村妇女的福利较高），而且还根据分析之前未预见的其他一些特征，特别是重要的地理影响异质性。我们的研究证明了CML方法在发现政策影响中意想不到的异质性方面的力量。我们对过去医疗保险扩张的评估结果可能会指导印度尼西亚补贴医疗保险资格标准的重新设计。
Policymakers seeking to target health policies efficiently towards specific population groups need to know which individuals stand to benefit the most from each of these policies. While traditional approaches for subgroup analyses are constrained to only consider a small number of pre-defined subgroups, recently proposed causal machine learning (CML) approaches help explore treatment-effect heterogeneity in a more flexible yet principled way. Causal forests use a generalisation of the random forest algorithm to estimate heterogenous treatment effects both at the individual and the subgroup level. Our paper aims to explore this approach in the setting of health policy evaluation with strong observed confounding, applied specifically to the context of mothers’ health insurance enrolment in Indonesia. Comparing two health insurance schemes (subsidised and contributory) against no insurance, we find beneficial average impacts of enrolment in contributory health insurance on maternal health care utilisation and infant mortality, but no impact of subsidised health insurance. The causal forest algorithm identified significant heterogeneity in the impacts of contributory insurance, not just along socioeconomic variables that we pre-specified (indicating higher benefits for poorer, less educated, and rural women), but also according to some other characteristics not foreseen prior to the analysis, suggesting in particular important geographical impact heterogeneity. Our study demonstrates the power of CML approaches to uncover unexpected heterogeneity in policy impacts. The findings from our evaluation of past health insurance expansions can potentially guide the re-design of the eligibility criteria for subsidised health insurance in Indonesia.