Estimating heterogeneous impacts Of subsidised health insurance: A causal machine learning approach

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. 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. We also find that the effect is modified by a set of theoretically motivated covariates and those identified through our data-driven approach.