A Locally Robust Semiparametric Approach to Examiner IV Designs

TL;DR

This paper introduces a robust semiparametric method for estimating causal effects with examiner IV designs, accommodating many examiners and covariates, and leveraging machine learning for improved estimation.

Contribution

It develops an orthogonal moment function that is robust to biases and misspecification, enabling valid estimation even with complex models and high-dimensional data.

Findings

Provides a framework for root-n consistent estimation with many covariates.

Allows use of machine learning techniques like neural networks and LASSO.

Ensures robustness to model misspecification and biases.

Abstract

I propose a locally robust semiparametric framework for estimating causal effects using the popular examiner IV design, in the presence of many examiners and possibly many covariates relative to the sample size. The key ingredient of this approach is an orthogonal moment function that is robust to biases and local misspecification from the first step estimation of the examiner IV. I derive the orthogonal moment function and show that it delivers multiple robustness where the outcome model or at least one of the first step components is misspecified but the estimating equation remains valid. The proposed framework not only allows for estimation of the examiner IV in the presence of many examiners and many covariates relative to sample size, using a wide range of nonparametric and machine learning techniques including LASSO, Dantzig, neural networks and random forests, but also delivers root-n consistent estimation of the parameter of interest under mild assumptions.