Interpretable Almost Matching Exactly With Instrumental Variables

Uncertainty in the estimation of the causal effect in observational studies is often due to unmeasured confounding, i.e., the presence of unobserved covariates linking treatments and outcomes. Instrumental Variables (IV) are commonly used to reduce the effects of unmeasured confounding. Existing methods for IV estimation either require strong parametric assumptions, use arbitrary distance metrics, or don't scale well to large datasets. We propose a matching framework for IV in the presence of observed categorical confounders that addresses all these weaknesses. Our method first matches units exactly, and then consecutively drops variables to approximately match the remaining units on as many variables as possible. We show that our algorithm constructs better quality matches than other existing methods on simulated datasets, and produces interesting and robust results in a real-world application.