Pairwise Valid Instruments

TL;DR

This paper introduces VSIV, a novel estimation method that identifies valid instrument pairs in heterogeneous causal models, improving LATE estimation by reducing bias and exploiting testable validity implications.

Contribution

The paper proposes VSIV, a new approach for estimating local average treatment effects with partially invalid instruments using pairwise validity and testable implications.

Findings

VSIV estimators are asymptotically normal under weak conditions.

VSIV reduces bias compared to standard LATE estimators.

Application to education returns demonstrates practical effectiveness.

Abstract

Finding valid instruments is difficult. We propose Validity Set Instrumental Variable (VSIV) estimation, a method for estimating local average treatment effects (LATEs) in heterogeneous causal effect models when the instruments are partially invalid. We consider settings with pairwise valid instruments, that is, instruments that are valid for a subset of instrument value pairs. VSIV estimation exploits testable implications of instrument validity to remove invalid pairs and provides estimates of the LATEs for all remaining pairs, which can be aggregated into a single parameter of interest using researcher-specified weights. We show that the proposed VSIV estimators are asymptotically normal under weak conditions and remove or reduce the asymptotic bias relative to standard LATE estimators (that is, LATE estimators that do not use testable implications to remove invalid variation). We evaluate the finite sample properties of VSIV estimation in application-based simulations and apply our method to estimate the returns to college education using parental education as an instrument.