If age is judged to be a major factor causing treatment effect to vary from individual to individual, then age differences between the sampled students and the general population would lead to a biased estimate of the average treatment effect in that population. Such bias can be corrected though by a simple re-weighing procedure: We take the age-specific effect in the student subpopulation and compute its average using the age distribution in the general population. This would give us an unbiased estimate of the average treatment effect in the population. If, on the other hand, the relevant factor that distinguishes the study sample from the general population is in itself affected by the treatment, then a different re-weighing scheme need be invoked. Calling this factor "Z", we again average the "z"-specific effect of "X" on "Y" in the experimental sample, but now we weigh it by the "causal effect" of "X" on "Z". In other words, the new weight is the proportion of units attaining level "Z=z" had treatment "X=x" been administered to the entire population. This interventional probability, often written formula_1, can sometimes be estimated from