  Propensity score methods were proposed by Rosenbaum and Rubin [Biometrika 70
(1983) 41--55] as central tools to help assess the causal effects of
interventions. Since their introduction more than two decades ago, they have
found wide application in a variety of areas, including medical research,
economics, epidemiology and education, especially in those situations where
randomized experiments are either difficult to perform, or raise ethical
questions, or would require extensive delays before answers could be obtained.
In the past few years, the number of published applications using propensity
score methods to evaluate medical and epidemiological interventions has
increased dramatically. Nevertheless, thus far, we believe that there have been
few applications of propensity score methods to evaluate marketing
interventions (e.g., advertising, promotions), where the tradition is to use
generally inappropriate techniques, which focus on the prediction of an outcome
from background characteristics and an indicator for the intervention using
statistical tools such as least-squares regression, data mining, and so on.
With these techniques, an estimated parameter in the model is used to estimate
some global ``causal'' effect. This practice can generate grossly incorrect
answers that can be self-perpetuating: polishing the Ferraris rather than the
Jeeps ``causes'' them to continue to win more races than the Jeeps
$\Leftrightarrow$ visiting the high-prescribing doctors rather than the
low-prescribing doctors ``causes'' them to continue to write more
prescriptions. This presentation will take ``causality'' seriously, not just as
a casual concept implying some predictive association in a data set, and will
illustrate why propensity score methods are generally superior in practice to
the standard predictive approaches for estimating causal effects.
