Estimation of causal effects from observational data is possible!

The claim that “correlation is not causation” is commonly heard or read in different scientific environments. However, for many years, economists have been applying a method that actually allows to do it: Instrumental Variable Regression (IVR). Our group has recently published a tutorial on Psychological Methods on how to do it within the framework of Structural Regression Model.

Here you are the abstract:

“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) 2020 APA, all rights reserved).”

A. Maydeu-Olivares, D. Shi, & Fairchild, A. J. (2020). Estimating causal effects in linear regression models with observational data: The instrumental variables regression model. Psychological methods, 25(2), 243–258. https://doi.org/10.1037/met0000226