An Instrumental Variable Tree Approach for Detecting Heterogeneous Treatment Effects in Observational Studies

Abstract

We develop a technique that incorporates the instrumental variable method into a causal tree to correct for potential endogeneity biases in heterogeneous treatment effect analysis using observational studies. The resulting instrumental variable tree approach partitions subjects into subgroups with similar treatment effects within subgroups and different treatment effects across subgroups. The estimated treatment effects are asymptotically consistent under very general assumptions. Using simulated data, we show that our approach has better coverage rates and smaller mean-squared errors than the conventional causal tree, and that a forest constructed using instrumental variable trees has better accuracy and interpretability than the generalized random forest.

Keywords: Heterogeneous treatment effect, big data analytics, machine learning, causal inference