mlsanityData to 7 Oct 2026

Who Increases Emergency Department Use? New Insights from the Oregon Health Insurance Experiment

arXiv 2201.07072 ↗First posted 18 Jan 2022econ.EM stat.MLcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 4 of its 55 references that are in the map. First posted on arXiv 18 Jan 2022.

Builds on

4 of 55 references.

References · 4

  1. 2015Estimation and Inference of Heterogeneous Treatment Effects using Random Forestscited by 597
  2. 2016Generalized Random Forestscited by 309
  3. 2019Machine Learning Methods Economists Should Know Aboutcited by 63
  4. 2020When Should We (Not) Interpret Linear IV Estimands as LATE?cited by 1

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Cited by year

Citing papers per year: 2023: 1.

1 paper.