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On Propensity Score Methodology

In: Statistics for Data Science and Policy Analysis

Author

Listed:
  • Paul Dewick

    (University of Canberra)

  • Shuangzhe Liu

    (University of Canberra)

Abstract

In an observational study, researchers are constantly required to distinguish the effects caused by the assignment of treatment. Propensity score methodology is one way to determine the effects of, and their probabilities, given a vector of observed covariates, which is particularly popular in the fields of medical, pharmaceutical and social sciences. However, there are mixed views for the best methodologies to use and an overall understanding of the propensity score methodology. Also, there is minimal literature for propensity score methods being used within the broader scientific community. Propensity score methodology can be suited to determine effects caused by, not only treatment of pharmaceutical medication, but for “treatment” of some external event, proposed event or interaction within the wider community. For example, the effect on a regional community due to business closure, or a road by-pass would be a reasonable case of how propensity score methods can be further used within the wider scientific community. The main objective of this paper is to demonstrate how propensity score methodology can be used to answer questions on effects caused by an external event or interaction on a community. The propensity score methodology will be given a framework that can be followed, explained and reported that will help allow for robust decision making, planning and policy decisions to be undertaken.

Suggested Citation

  • Paul Dewick & Shuangzhe Liu, 2020. "On Propensity Score Methodology," Springer Books, in: Azizur Rahman (ed.), Statistics for Data Science and Policy Analysis, chapter 0, pages 41-53, Springer.
    • Handle: RePEc:spr:sprchp:978-981-15-1735-8_4
    DOI: 10.1007/978-981-15-1735-8_4
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