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Estimating the Causal Effects of Marketing Interventions Using Propensity Score Methodology

Donald B. Rubin and Richard P. Waterman
Statistical Science
Vol. 21, No. 2, A Special Issue on Statistical Challenges and Opportunities in Electronic Commerce Research (May, 2006), pp. 206-222
Stable URL: http://www.jstor.org/stable/27645750
Page Count: 17
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Since scans are not currently available to screen readers, please contact JSTOR User Support for access. We'll provide a PDF copy for your screen reader.
Estimating the Causal Effects of Marketing Interventions Using Propensity Score Methodology
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Abstract

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 ⇔ 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.

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