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Using the Propensity Score Method to Estimate Causal Effects: A Review and Practical Guide
Abstract
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 causal 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.
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Metadata
- Authors
- Li, M.
- Venue
- Organizational Research Methods
- Publisher
- SAGE Publications
- Published
- 2012-06-13
- Volume
- 16
- Issue
- 2
- Pages
- 188-226
- ISSN
- 1094-4281 (ISSN-L)
- 1552-7425
- OpenAlex
- W2126600133
- Cited by
- 366
- References
- 86
- FWCI
- 5.39
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
