Detecting heterogeneous treatment effects with instrumental variables and application to the Oregon health insurance experiment
Michael Johnson, Jiongyi Cao, Hyunseung Kang
The Annals of Applied Statistics · 2022 · Volume 16 · Issue 2
Abstract
There is an increasing interest in estimating heterogeneity in causal effects in randomized and observational studies. However, little research has been conducted to understand effect heterogeneity in an instrumental variables study. In this work we present a method to estimate heterogeneous causal effects using an instrumental variable with matching. The method has two parts. The first part uses subject-matter knowledge and interpretable machine-learning techniques, such as classification and regression trees, to discover potential effect modifiers. The second part uses closed testing to test for statistical significance of each effect modifier while strongly controlling the familywise error rate. We apply this method on the Oregon Health Insurance Experiment, estimating the effect of Medicaid on the number of days an individual’s health does not impede their usual activities by using a randomized lottery as an instrument. Our method revealed Medicaid’s effect was most impactful among older, English-speaking, non-Asian males and younger, English-speaking individuals with, at most, a high school diploma or General Educational Development.
Keywords
Causal inference, complier average causal effect, heterogeneous treatment, instrumental variables, machine learning, Matching, Oregon Health Insurance Experiment
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