Inference on LATEs with covariates

2024/02/20 by Tom Boot, Boot, Tom, Didier Nibbering +1 · 3 citations
Computer Science · Economics, Econometrics and Finance · Psychology · #Diverse Scientific and Economic Studies #Econometrics (econ.EM) #FOS: Economics and business #Phonetics and Phonology Research #Wireless Communication Networks Research

paper · pdf · doi:10.48550/arxiv.2402.12607

openalex publication_date 2024/02/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

In theory, two-stage least squares (TSLS) identifies a weighted average of covariate-specific local average treatment effects (LATEs) from a saturated specification, without making parametric assumptions on how available covariates enter the model. In practice, TSLS is severely biased as saturation leads to a large number of control dummies and an equally large number of, arguably weak, instruments. This paper derives asymptotically valid tests and confidence intervals for the weighted average of LATEs that is targeted, yet missed by saturated TSLS. The proposed inference procedure is robust to unobserved treatment effect heterogeneity, covariates with rich support, and weak identification. We find LATEs statistically significantly different from zero in applications in criminology, finance, health, and education.

Cited by

• An Improved Inference for IV Regressions
• Robust Inference with High-Dimensional Instruments
• Wild Bootstrap Inference for Linear Regressions with Many Covariates

Related

• Interacted two-stage least squares with treatment effect heterogeneity
• Point Identification of LATE with Two Imperfect Instruments
• Estimation of Local Average Treatment Effect by Data Combination
• A Practical Guide to Instrumental Variables Methods with Heterogeneous Treatment Effects
• Estimating the Local Average Treatment Effect Without the Exclusion Restriction