We use cross-sectional data from the 2014 American Community Survey to estimate the heterogeneous treatment effects of expanding Medicaid on health insurance coverage. In doing so, we provide robust evidence which can help policymakers target future Medicaid policies towards particularly responsive individuals. Medicaid expansions were optional by state, allowing us to identify treatment effects by comparing expansion and non-expansion states. We then estimate heterogeneous treatment effects using a non-parametric machine learning algorithm called a causal forest, which offers several advantages over prior methods in the literature. Most notably, it provides a systematic means to discover, from the data, which variables are most relevant for modelling heterogeneity. We find strong evidence of heterogeneity in our estimated treatment effects. Furthermore, we find that individuals with the largest treatment effects were typically aged 25-34; had a high school diploma as their highest level of education; spoke a language other than English at home; and/or were in private for-profit employment.