Because we can never be sure whether a given set of controls is enough to eliminate selection bias, it’s important to ask how sensitive regression results are to changes in the list of controls. Our confidence in regression estimates of causal effects grows when treatment effects are insensitive—masters say “robust”—to whether a particular variable is added or dropped as long as a few core controls are always included in the model.
