  Control variables are included in regression analyses to estimate the causal
effect of a treatment on an outcome. In this paper, we argue that the estimated
effect sizes of controls are unlikely to have a causal interpretation
themselves, though. This is because even valid controls are possibly endogenous
and represent a combination of several different causal mechanisms operating
jointly on the outcome, which is hard to interpret theoretically. Therefore, we
recommend refraining from interpreting marginal effects of controls and
focusing on the main variables of interest, for which a plausible
identification argument can be established. To prevent erroneous managerial or
policy implications, coefficients of control variables should be clearly marked
as not having a causal interpretation or omitted from regression tables
altogether. Moreover, we advise against using control variable estimates for
subsequent theory building and meta-analyses.
