Who benefits from public health insurance in Indonesia? A machine learning approach to estimate treatment effect heterogeneity

Noemi Kreif (University of York)

Researchers evaluating the effects of health policies are often interested in identifying individuals who would benefit most from a particular programme. Such analyses could provide evidence on whether a programme worked for the intended recipients, and help design the eligibility criteria of future programmes. Traditional approaches such as subgroup analyses are constrained by only considering a few, pre-specified effect modifiers, and can also be prone to cherry-picking by ad-hoc selection of subgroups. Recently proposed causal inference approaches that incorporate machine learning (ML) have the potential to help explore treatment-effect heterogeneity in a flexible yet principled way. In this talk I illustrate such an approach, Causal Forests (Athey et al. 2019), in an evaluation of the effect of public health insurance on health care utilisation of Indonesian women. I highlight the opportunities presented by the approach to identify subgroups where the impacts of having health insurance differ, and to estimate so-called conditional average treatment effects at the level of the individual. I also discuss the challenges of using this approach alongside non-randomised study designs, typical when evaluating large scale health policies.