Causal Analysis in Theory and Practice

L.B.S., from University of Arizona, questioned whether the do(x) operator can represent realistic actions or experiments: "Even an otherwise perfectly executed randomized experiment may yield perfectly misleading conclusions if, for example, the construct validity of the treatment is zero, e.g., there is a serious confounding. A good example is a study involving injected vitamin E as a treatment for incubated children at risk for retrolental fibroplasia. The randomized experiment indicated efficacy for the injections, but it was soon discovered that the actual effective treatment was opening the pressurized, oxygen-saturated incubators several times per day to give the injections, thus lowering the barometric pressure and oxygen levels in the blood of the infants (Leonard, Major Medical Mistakes). Any statistical analysis would have been misleading in that case."

S.M., from Georgia Institute of Technology, adds:
"Your example of the misleading causal effect, shows the kind of thing that troubles me about the do(x) concept. You do(x) or don't do(x) but something else, and this seems correlated with an effect. But it may be something else that is correlated with do(x) that is the cause and not the do(x) per se."