If we do have measurements of the third variable, then it is very easy to deconfound the true and spurious effects. For instance, if the confounding variable Z is age, we compare the treatment and control groups in every age group separately. We can then take an average of the effects, weighting each age group according to its percentage in the target population. This method of compensation is familiar to all statisticians; it is called “adjusting for Z” or “controlling for Z.”
