A Practical Guide to Instrumental Variables Methods with Heterogeneous Treatment Effects

TL;DR

This paper offers a practical overview of instrumental variables methods for heterogeneous treatment effects, emphasizing specification choices, robustness checks, and software tools for empirical application.

Contribution

It bridges recent theoretical developments in LATE with practical guidance on implementation, testing, and robustness in empirical research.

Findings

Different specifications lead to distinct weighted averages of covariate-specific LATEs.

Parametric misspecification can undermine causal interpretation.

Software implementations facilitate practical application of IV methods.

Abstract

Instrumental variables (IV) methods are central to applied microeconomics. While classical approaches assume linear models with constant effects, recent literature has shifted toward the local average treatment effect (LATE) framework to accommodate heterogeneous treatment effects. This paper provides a practical guide to aligning empirical practice with recent theory. We first examine how different specifications with covariates lead to distinct weighted averages of covariate-specific LATEs. We then discuss how parametric misspecification can undermine the causal interpretation of these estimands and suggest flexible specifications as essential robustness checks. Finally, we review formal tests for LATE assumptions and methods robust to monotonicity violations. We provide a guide to software implementations to help researchers apply the methods in practice.