Using the Propensity Score Method to Estimate Causal Effects: A Review and Practical Guide


Evidence-based management requires management scholars to draw causal inferences. Researchers generally rely on observational data sets and regression models where the independent variables have not been exogenously manipulated to estimate causal effects; however, using such models on observational data sets can produce a biased effect size of treatment intervention. This article introduces the propensity score method (PSM)—which has previously been widely employed in social science disciplines such as public health and economics—to the management field. This research reviews the PSM literature, develops a procedure for applying the PSM to estimate the cau-sal effects of intervention, elaborates on the procedure using an empirical example, and discusses the potential application of the PSM in different management fields. The implementation of the PSM in the management field will increase researchers ’ ability to draw causal inferences using observational data sets. Keywords causal effect, propensity score method, matching Management scholars are interested in drawing causal inferences (Mellor & Mark, 1998). One example of a causal inference that researchers might try to determine is whether a specific manage-ment practice, such as group training or a stock option plan, increases organizational performance. Typically, management scholars rely on observational data sets to estimate causal effects of the management practice. Yet, endogeneity—which occurs when a predictor variable correlates with the error term—prevents scholars from drawing correct inferences (Antonakis, Bendahan, Jacquart, & Lalive, 2010; Wooldridge, 2002). Econometricians have proposed a number of techniques to dea

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