Estimating the Causal Effects of Marketing Interventions Using Propensity Score Methodology

By Donald B. Rubin, Richard P. Waterman. 2006

91 citations recorded by the source corpus.

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, es- pecially in those situations where randomized experiments are either difficult to perform, or raise ethical questions, or would require exten- sive 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 dra- matically. Nevertheless, thus far, we believe that there have been few applications of propensity score methods to evaluate marketing inter- ventions (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 in- tervention 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 doc- tors rather than the low-prescribing doctors "causes" them to continue to write more prescriptions. This presentation will take "causality" seri- ously, not just as a casual concept implying some predictive association in a data set, and will illustrate why propensity score methods are gen- erally superior in practice to the standard predictive approaches for estimating causal effects.

Subfields: Sociology and Political Science, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/085142aa446928393569b0f80174b28e7b50cd04

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Citations in the corpus

Do CRM Systems Cause One-to-One Marketing Effectiveness? (2006, background): “As Rubin and Waterman (2006) elaborate, the causal effect is not a change in time of the observed outcome: instead, it is the difference between two potential outcomes, only one of which is observed.”
Do Auction Parameters Affect Buyer Surplus in E‐Auctions for Procurement? (2007, methodology): “…would be useful to employ a potential outcomes-based propensity score approach in line with recent work that shows applicability of this method in information systems and e-commerce literature (e.g., Mithas, Almirall, and Krishnan 2006a; Mithas, Krishnan, and Fornell 2005; Rubin and Waterman 2006).”
Evaluating online ad campaigns in a pipeline: causal models at scale (2010, methodology): “In a careful analysis of one set of data, Rubin and Waterman [21] used stratiﬁed propensity scores to evaluate the eﬀect of visits from pharmaceutical sales teams on the number of prescriptions that physicians write.”
For objective causal inference, design trumps analysis (2008, background): “Readers interested in the conclusions, which are a bit surprising, should check Rubin and Waterman (2006).”
Emergence of Financial Intermediaries in Electronic Markets: The Case of Online P2P Lending (2009, unknown): “One possible way of easing the problem lies in the matching method (Rubin and Waterman 2006), by which one can construct pairs of comparable credit transactions with and without using intermediaries.”