On Deep Instrumental Variables Estimate

Understand

The endogeneity issue is fundamentally important as many empirical applications may suffer from the omission of explanatory variables, measurement error, or simultaneous causality.

• Recently, \cite{hllt17} propose a "Deep Instrumental Variable (IV)" framework based on deep neural networks to address endogeneity, demonstrating superior performances than existing approaches.
• The aim of this paper is to theoretically understand the empirical success of the Deep IV.
• Specifically, we consider a two-stage estimator using deep neural networks in the linear instrumental variables model.