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.
翻译：传统工具变量（IV）方法常面临工具变量弱或无效的问题，且严重依赖外部数据。本文提出一种合成工具变量（SIV）方法，仅利用现有数据即可构建有效工具变量。该方法基于数据驱动的双趋势（DT）条件识别有效工具变量，无需引入外部变量。SIV对异方差性具有鲁棒性，且能确定内生回归变量与误差项之间相关性的真实方向——这一假设在实证研究中通常被预先设定。通过模拟实验和实际应用，我们证明SIV能够缓解常见工具变量方法的局限性，减少对稀缺工具变量的依赖，从而改进因果推断。该方法对经济学、流行病学及政策评估领域具有广泛意义。
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