## Estimating the causal effects of marketing interventions using propensity score methodology.(English)Zbl 1426.62325

Summary: Propensity score methods were proposed by P. R. Rosenbaum and D. B. Rubin [Biometrika 70, 41–55 (1983; Zbl 0522.62091)] 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.

### MSC:

 62P10 Applications of statistics to biology and medical sciences; meta analysis

Zbl 0522.62091
Full Text:

### References:

 [1] Bucklin, R., Lattin, J., Ansari, A., Gupta, S., Bell, D., Coupey, E., Little, J., Mela, C., Montgomery, A. and Steckel, J. (2002). Choice and the Internet: From clickstream to research stream. Marketing Letters 13 245–258. [2] Cochran, W. G. and Rubin, D. B. (1973). Controlling bias in observational studies: A review. Sankhyā Ser. A 35 417–446. · Zbl 0291.62012 [3] Dehejia, R. H. and Wahba, S. (1999). Causal effects in nonexperimental studies: Re-evaluating the evaluation of training programs. J. Amer. Statist. Assoc. 94 1053–1062. [4] Gu, X. S. and Rosenbaum, P. R. (1993). Comparison of multivariate matching methods: Structures, distances, and algorithms. J. Comput. Graph. Statist. 2 405–420. [5] Holland, P. (1986). Statistics and causal inference (with discussion). J Amer. Statist. Assoc. 81 945–970. JSTOR: · Zbl 0607.62001 [6] Imai, K. and van Dyk, D. A. (2004). Causal inference with general treatment regimes: Generalizing the propensity score. J. Amer. Statist. Assoc. 99 854–866. · Zbl 1117.62361 [7] Imbens, G. W. (2000). The role of the propensity score in estimating dose-response functions. Biometrika 87 706–710. JSTOR: · Zbl 1120.62334 [8] Imbens, G. W. and Rubin, D. B. (2006). Rubin causal model. In The New Palgrave Dictionary of Economics , 2nd ed. Palgrave McMillan, New York. [9] LaLonde, R. (1986). Evaluating the econometric evaluations of training programs with experimental data. Amer. Economic Review 76 604–620. [10] Ming, K. and Rosenbaum, P. R. (2000). Substantial gains in bias reduction from matching with a variable number of controls. Biometrics 56 118–124. · Zbl 1060.62641 [11] Montgomery, A. L., Shibo, L., Srinivasan, K. and Liechty, J. C. (2004). Modeling online browsing and path analysis using clickstream data. Marketing Sci. 23 579–595. [12] Rosenbaum, P. R. (1989). Optimal matching for observational studies. J. Amer. Statist. Assoc. 84 1024–1032. [13] Rosenbaum, P. R. and Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika 70 41–55. JSTOR: · Zbl 0522.62091 [14] Rosenbaum, P. R. and Rubin, D. B. (1984). Reducing bias in observational studies using subclassification on the propensity score. J. Amer. Statist. Assoc. 79 516–524. [15] Rubin, D. B. (1990). Comment: Neyman (1923) and causal inference in experiments and observational studies. Statist. Sci. 5 472–480. · Zbl 0955.01559 [16] Rubin, D. B. (1997). Estimating causal effects from large data sets using propensity scores. Ann. Internal Medicine 127 757–763. [17] Rubin, D. B. (2001). Using propensity scores to help design observational studies: Application to the tobacco litigation. Health Services and Outcomes Research Methodology 2 169–188. [18] Rubin, D. B. (2005). Causal inference using potential outcomes: Design, modeling, decisions. J. Amer. Statist. Assoc. 100 322–331. · Zbl 1117.62418 [19] Rubin, D. B. (2006). Estimating treatment effects from nonrandomized studies using subclassification on propensity scores. In Advances in Social and Organizational Psychology : A Tribute to Ralph Rosnow (D. A. Hantula, ed.) 41–59. Erlbaum, Mahwah, NJ. [20] Rubin, D. B. and Thomas, N. (2000). Combining propensity score matching with additional adjustments for prognostic covariates. J. Amer. Statist. Assoc. 95 573–585. [21] Singh, V., Hansen, K. and Blattberg, R. (2006). Market entry and consumer behavior: An investigation of a Wal-Mart Supercenter. Marketing Sci.
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. It attempts to reflect the references listed in the original paper as accurately as possible without claiming the completeness or perfect precision of the matching.