Double Robustness for Complier Parameters and a Semiparametric Test for Complier Characteristics

TL;DR

This paper introduces a semiparametric test to assess whether different instruments produce comparable complier subpopulations and if compliers resemble the full population, enhancing the robustness and external validity of instrumental variable analyses.

Contribution

It develops a novel semiparametric testing framework for comparing complier characteristics across instruments and extends doubly robust methods to complier parameters.

Findings

The test can evaluate the external validity of instruments.

It provides a reinterpretation of differences in LATE estimates.

The approach incorporates machine learning updates for weighting.

Abstract

We propose a semiparametric test to evaluate (i) whether different instruments induce subpopulations of compliers with the same observable characteristics on average, and (ii) whether compliers have observable characteristics that are the same as the full population on average. The test is a flexible robustness check for the external validity of instruments. We use it to reinterpret the difference in LATE estimates that Angrist and Evans (1998) obtain when using different instrumental variables. To justify the test, we characterize the doubly robust moment for Abadie (2003)'s class of complier parameters, and we analyze a machine learning update to $\kappa$ weighting.