We provide new insights regarding the headline result that Medicaid increased emergency department (ED) use from the Oregon experiment. We find meaningful heterogeneous impacts of Medicaid on ED use using causal machine learning methods. The individualized treatment effect distribution includes a wide range of negative and positive values, suggesting the average effect masks substantial heterogeneity. A small group-about 14% of participants-in the right tail of the distribution drives the overall effect. We identify priority groups with economically significant increases in ED usage based on demographics and previous utilization. Intensive margin effects are an important driver of increases in ED utilization.

翻译：我们针对俄勒冈州实验得出的“医疗补助增加了急诊科使用量”这一核心结果提供了新见解。通过运用因果机器学习方法，我们发现了医疗补助对急诊科使用量具有显著异质性影响。个体化治疗效应分布包含正负值的广泛区间，表明平均效应掩盖了实质性的异质性。分布右尾端约14%的少数参与者群体主导了整体效应。基于人口统计特征和既往使用记录，我们识别出急诊科使用量具有经济学显著增长的优先群体。集约边际效应是急诊科使用量增加的重要驱动因素。