Drawing Causal Inferences Using Propensity Scores: A Practical Guide for Community Psychologists

Stephanie T. Lanza, Julia E. Moore, Nicole M. Butera

Research output: Contribution to journalArticlepeer-review

47 Scopus citations

Abstract

Confounding present in observational data impede community psychologists' ability to draw causal inferences. This paper describes propensity score methods as a conceptually straightforward approach to drawing causal inferences from observational data. A step-by-step demonstration of three propensity score methods-weighting, matching, and subclassification-is presented in the context of an empirical examination of the causal effect of preschool experiences (Head Start vs. parental care) on reading development in kindergarten. Although the unadjusted population estimate indicated that children with parental care had substantially higher reading scores than children who attended Head Start, all propensity score adjustments reduce the size of this overall causal effect by more than half. The causal effect was also defined and estimated among children who attended Head Start. Results provide no evidence for improved reading if those children had instead received parental care. We carefully define different causal effects and discuss their respective policy implications, summarize advantages and limitations of each propensity score method, and provide SAS and R syntax so that community psychologists may conduct causal inference in their own research.

Original languageEnglish (US)
Pages (from-to)380-392
Number of pages13
JournalAmerican Journal of Community Psychology
Volume52
Issue number3-4
DOIs
StatePublished - Dec 2013

All Science Journal Classification (ASJC) codes

  • Health(social science)
  • Applied Psychology
  • Public Health, Environmental and Occupational Health

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