Near/Far Matching - A Nonparametric Instrumental Variables Technique for Binary Outcomes

Instrumental variables (IV) is a framework for making causal inferences about the effect of a treatment based on an observational study in which there are unmeasured confounding variables. The most common form of IV estimation is two-stage least squares (2SLS), which works well when the outcome of interest is continuous. However, in many policy settings the objective is to estimate the effect of a treatment on a binary outcome. We propose a nonparametric matching technique near/far matching which is capable of estimating population level treatment effects when the outcome is binary. We provide a test statistic with standard errors. Our method also allows us to manipulate the strength of the instrument. We illustrate our method using a study of neonatal intensive care units (NICUs) treatment effect on premature babies (preemies) born in Pennsylvania.