A Practical Guide to Instrumental Variables Methods with Heterogeneous Treatment Effects

2026-05-14

AI Summary

This study addresses the limitation of conventional instrumental variable (IV) methods, which often assume a constant treatment effect and thus struggle to accommodate effect heterogeneity in real-world settings. Building on the local average treatment effect (LATE) framework, the paper systematically integrates covariate-adjusted IV approaches, clarifying how covariates influence the weighting structure of LATE estimators. It proposes flexible modeling strategies to avoid parametric misspecification and incorporates robust diagnostic tests for violations of the monotonicity assumption. By combining nonparametric and semiparametric estimation techniques, formal hypothesis testing, and accompanying software implementation, this work offers empirical researchers a theoretically rigorous yet practically feasible causal inference workflow, substantially enhancing the reliability and applicability of IV analysis.

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.

Problem

• Instrumental Variables
• Heterogeneous Treatment Effects
• Local Average Treatment Effect
• Causal Inference
• Empirical Practice

Innovation

• Instrumental Variables
• Heterogeneous Treatment Effects
• Local Average Treatment Effect
• Parametric Misspecification
• Monotonicity Robustness