Estimating the Causal Effects of Marketing Interventions Using Propensity Score Methodology

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.Comment: Published at http://dx.doi.org/10.1214/088342306000000259 in the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

A Machine Learning Compatible Method For ordinal Propensity Score Stratification and Matching

Although machine learning techniques that estimate propensity scores for observational studies with multivalued treatments have advanced rapidly in recent years, the development of propensity score adjustment techniques has not kept pace. While machine learning propensity models provide numerous benefits, they do not produce a single variable balancing score that can be used for propensity score stratification and matching. This issue motivates the development of a flexible ordinal propensity scoring methodology that does not require parametric assumptions for the propensity model. The proposed method fits a one-parameter power function to the cumulative distribution function (CDF) of the generalized propensity score (GPS) vector resulting from any machine learning propensity model, and is henceforth called the GPS-CDF method. The estimated parameter from the GPS-CDF method

Propensity score methodology for confounding control in health care utilization databases

Propensity score (PS) methodology is a common approach to control for confounding in nonexperimental studies of treatment effects using health care utilization databases. This methodology offers researchers many advantages compared with conventional multivariate models: it directly focuses on the determinants of treatment choice, facilitating the understanding of the clinical decision-making process by the researcher; it allows for graphical comparisons of the distribution of propensity scores and truncation of subjects without overlapping PS indicating a lack of equipoise; it allows transparent assessment of the confounder balance achieved by the PS at baseline; and it offers a straightforward approach to reduce the dimensionality of sometimes large arrays of potential confounders in utilization databases, directly addressing the “curse of dimensionality” in the context of rare events. This article provides an overview of the use of propensity score methodology for pharmacoepidemiologic research with large health care utilization databases, covering recent discussions on covariate selection, the role of automated techniques for addressing unmeasurable confounding via proxies, strategies to maximize clinical equipoise at baseline, and the potential of machine-learning algorithms for optimized propensity score estimation. The appendix discusses the available software packages for PS methodology. Propensity scores are a frequently used and versatile tool for transparent and comprehensive adjustment of confounding in pharmacoepidemiology with large health care databases

Evaluating the impact of public subsidies on a firm's performance: A quasi-experimental approach

Many regional governments in developed countries design programs to improve the competitiveness of local firms. In this paper, we evaluate the effectiveness of public programs whose aim is to enhance the performance of firms located in Catalonia (Spain). We compare the performance of publicly subsidised companies (treated) with that of similar, but unsubsidised companies (non-treated). We use the Propensity Score Matching (PSM) methodology to construct a control group which, with respect to its observable characteristics, is as similar as possible to the treated group, and that allows us to identify firms which retain the same propensity to receive public subsidies. Once a valid comparison group has been established, we compare the respective performance of each firm. As a result, we find that recipient firms, on average, change their business practices, improve their performance, and increase their value added as a direct result of public subsidy programs.Public policy, evaluation studies, firm performance, propensity Score Matching.

Evaluating the Impact of Public Subsidies on a Firms Performance: a Quasi-experimental Approach

Many regional governments in developed countries design programs to improve the competitiveness of local firms. In this paper, we evaluate the effectiveness of public programs whose aim is to enhance the performance of firms located in Catalonia (Spain). We compare the performance of publicly subsidised companies (treated) with that of similar, but unsubsidised companies (non-treated). We use the Propensity Score Matching (PSM) methodology to construct a control group which, with respect to its observable characteristics, is as similar as possible to the treated group, and that allows us to identify firms which retain the same propensity to receive public subsidies. Once a valid comparison group has been established, we compare the respective performance of each firm. As a result, we find that recipient firms, on average, change their business practices, improve their performance, and increase their value added as a direct result of public subsidy programs.public policy, evaluation studies, firm performance, propensity score matching

Propensity score methodology for nonignorable nonresponse

When data are not missing at random, approaches to reduce nonresponse bias include subsampling nonresponding units and modeling. The objective of this thesis is to develop unbiased and precise model-assisted estimators of the population total that are applicable to data from a complex survey design with nonignorable nonresponse. When information from a nonrespondent subsample is available, weighting methods for missing-at-random data may be modified to reduce bias from nonignorable missingness in estimates of population totals. Propensity score methodology for nonignorable missingness is developed for use with the weighting class adjustment and with the Horvitz-Thompson estimator to account for the dependence between the outcome of interest and the response mechanism. The novel propensity score techniques for nonignorable nonresponse are applied to a binary outcome subject to nonignorable missingness from a complex survey of elk hunters and are also examined with simulation

Augmentation of Propensity Scores for Medical Records-based Research

Therapeutic research based on electronic medical records suffers from the possibility of various kinds of confounding. Over the past 30 years, propensity scores have increasingly been used to try to reduce this possibility. In this article a gap is identified in the propensity score methodology, and it is proposed to augment traditional treatment-propensity scores with outcome-propensity scores, thereby removing all other aspects of common causes from the analysis of treatment effects