Detecting Heterogeneous Treatment Effect with Instrumental Variables
This addresses the gap in understanding heterogeneity in instrumental variables studies, which is important for researchers in causal inference, but it is incremental as it builds on existing instrumental variable and machine learning techniques.
The paper tackles the problem of estimating heterogeneous causal effects in instrumental variables studies, presenting a method that combines interpretable machine learning with closed testing to identify and validate effect modifiers, and applies it to the Oregon Health Insurance Experiment to find evidence of heterogeneity in specific demographic groups.
There is an increasing interest in estimating heterogeneity in causal effects in randomized and observational studies. However, little research has been conducted to understand heterogeneity in an instrumental variables study. In this work, we present a method to estimate heterogeneous causal effects using an instrumental variable approach. 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 the statistical significance of the effect modifiers while strongly controlling familywise error rate. We conducted 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, and found evidence of heterogeneity in older men who prefer English and don't self-identify as Asian and younger individuals who have at most a high school diploma or GED and prefer English.