Bad Controls are those variables when added to the regression equation will impose bias or other disparities between the real effect size to be estimated, and the regression coefficient that represents it. They are defined on the contrary to good controls (i.e., confounders or deconfounders), which should be adjusted (controlled) in a regression model to improve the accuracy of estimating the desired parameters. Bad and Good Controls are necessary to be considered when deciding which variables to control for in regression analysis for causal inference.