Causal inference using regression-based statistical control: Confusion in Econometrics

Abstract Regression is a widely used econometric tool in research. In observational studies, based on number of assumptions, regression-based statistical control methods attempt to analyze the causation between treatment and outcome by adding variables. However, this approach may not produce reliable estimates causal effects. addition shortcomings method, lack confidence mainly related ambiguous formulations econometrics, such as definition selection bias, core variables, method testing for robustness. Within framework models, we clarify assumption inference using controls, described discuss how select variables satisfy conduct robustness tests regression estimates.