Instrumental variable methods are an underutilized tool to enhance causal inference in psychology. By way of incorporating predictors of the predictors (called "instruments" in the econometrics literature) into the model, instrumental variable regression (IVR) is able to draw causal inferences of a predictor on an outcome. We show that by regressing the outcome y on the predictors x and the predictors on the instruments, and modeling correlated disturbance terms between the predictor and outcome, causal inferences can be drawn on y on x if the IVR model cannot be rejected in a structural equation framework. We provide a tutorial on how to apply this model using ML estimation as implemented in structural equation modeling (SEM) software. We additionally provide code to identify instruments given a theoretical model, to select the best subset of instruments when more than necessary are available, and we guide researchers on how to apply this model using SEM. Finally, we demonstrate how the IVR model can be estimated using a number of estimators developed in econometrics (e.g., 2-stage least squares regression) and point out that the latter is simply a multistage SEM estimator of the IVR model. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

中文翻译：

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使用观测数据估算线性回归模型中的因果效应：工具变量回归模型。

工具变量方法是一种未能充分利用的工具，可以增强心理学中的因果推理。通过将预测变量的预测变量（计量经济学文献中称为“工具”）合并到模型中，工具变量回归（IVR）能够得出结果的预测变量的因果推论。我们表明，通过对预测变量x和工具预测变量上的结果y进行回归，并对预测变量和结果之间的相关干扰项进行建模，如果IVR模型不能在结构方程中被拒绝，则可以对x上的y进行因果推断。框架。我们提供了有关如何使用结构方程模型（SEM）软件中实现的ML估计应用此模型的教程。我们还会提供代码，以识别具有理论模型的工具，在必要时选择最佳的仪器子集，我们指导研究人员如何使用SEM应用此模型。最后，我们演示了如何使用计量经济学中开发的许多估计器（例如，两阶段最小二乘回归）来估计IVR模型，并指出后者只是IVR模型的多阶段SEM估计器。（PsycINFO数据库记录（c）2019 APA，保留所有权利）。