Using Policy Learning to Inform Health Insurance Targeting: A Case Study of Indonesia
利用最优政策学习方法，基于印尼调查数据制定规则，将两种政府补贴保险定向分配给不同家庭，以降低灾难性医疗支出风险，并发现当前分配策略可被优化。
This paper demonstrates how optimal policy learning can inform the targeted allocation of Indonesia's two subsidized health insurance programmes. Using national survey data, we develop policy rules aimed at minimizing "catastrophic health expenditure" among enrollees of APBD or APBN, the two government-funded schemes. Employing a super learner ensemble approach, we use regression and machine learning methods of varying complexity to estimate conditional average treatment effects and construct policy rules to optimize program benefits, both with and without budget constraints. We find that the financial impact of APBD enrollment over APBN differs with household characteristics, particularly demographic composition, socioeconomic status, and geography. Households assigned to APBD under the policy rule are typically urban-based with better facilities, whereas rural households with less accessible healthcare are assigned to APBN-a pattern intensified under budget constraints. Both constrained and unconstrained optimal policy assignments show lower expected catastrophic expenditure risk than the current assignment strategy. This study contributes to the literature on heterogeneous treatment effects, optimal policy leaning, and health financing in developing countries, showcasing data-driven solutions for more equitable resource allocation in public health insurance contexts.