Causal Gradient Boosting: Boosted Instrumental Variable Regression

TL;DR

This paper introduces boostIV, a novel gradient boosting algorithm designed for instrumental variable regression, effectively correcting endogeneity bias without requiring prior assumptions on the target function or instruments, and demonstrating superior performance in simulations.

Contribution

The paper presents boostIV, an innovative data-driven gradient boosting method for IV regression that is consistent and outperforms existing approaches in finite samples.

Findings

boostIV is consistent under mild conditions.

It outperforms existing IV methods in simulations.

It is comparable to or better than recent methods in finite samples.

Abstract

Recent advances in the literature have demonstrated that standard supervised learning algorithms are ill-suited for problems with endogenous explanatory variables. To correct for the endogeneity bias, many variants of nonparameteric instrumental variable regression methods have been developed. In this paper, we propose an alternative algorithm called boostIV that builds on the traditional gradient boosting algorithm and corrects for the endogeneity bias. The algorithm is very intuitive and resembles an iterative version of the standard 2SLS estimator. Moreover, our approach is data driven, meaning that the researcher does not have to make a stance on neither the form of the target function approximation nor the choice of instruments. We demonstrate that our estimator is consistent under mild conditions. We carry out extensive Monte Carlo simulations to demonstrate the finite sample performance of our algorithm compared to other recently developed methods. We show that boostIV is at worst on par with the existing methods and on average significantly outperforms them.