A Synthetic Instrumental Variable Method: Using the Dual Tendency Condition for Coplanar Instruments

TL;DR

This paper introduces a Synthetic Instrumental Variable (SIV) method that constructs valid instruments from existing data using a dual tendency condition, improving causal inference without external instruments.

Contribution

The paper presents a novel data-driven SIV approach that identifies valid instruments solely from data, addressing weak and invalid instrument issues in causal analysis.

Findings

SIV is robust to heteroscedasticity.

SIV accurately determines the sign of correlations.

SIV outperforms traditional IV methods in simulations and real data.

Abstract

Traditional instrumental variable (IV) methods often struggle with weak or invalid instruments and rely heavily on external data. We introduce a Synthetic Instrumental Variable (SIV) approach that constructs valid instruments using only existing data. Our method leverages a data-driven dual tendency (DT) condition to identify valid instruments without requiring external variables. SIV is robust to heteroscedasticity and can determine the true sign of the correlation between endogenous regressors and errors--an assumption typically imposed in empirical work. Through simulations and real-world applications, we show that SIV improves causal inference by mitigating common IV limitations and reducing dependence on scarce instruments. This approach has broad implications for economics, epidemiology, and policy evaluation.