Using full matching to estimate causal effects in nonexperimental studies: examining the relationship between adolescent marijuana use and adult outcomes

Dev Psychol. 2008 Mar;44(2):395-406. doi: 10.1037/0012-1649.44.2.395.

Abstract

Matching methods such as nearest neighbor propensity score matching are increasingly popular techniques for controlling confounding in nonexperimental studies. However, simple k:1 matching methods, which select k well-matched comparison individuals for each treated individual, are sometimes criticized for being overly restrictive and discarding data (the unmatched comparison individuals). The authors illustrate the use of a more flexible method called full matching. Full matching makes use of all individuals in the data by forming a series of matched sets in which each set has either 1 treated individual and multiple comparison individuals or 1 comparison individual and multiple treated individuals. Full matching has been shown to be particularly effective at reducing bias due to observed confounding variables. The authors illustrate this approach using data from the Woodlawn Study, examining the relationship between adolescent marijuana use and adult outcomes.

Publication types

  • Research Support, N.I.H., Extramural

MeSH terms

  • Adolescent
  • Adult
  • African Americans / psychology*
  • African Americans / statistics & numerical data
  • Chicago
  • Educational Status*
  • Female
  • Humans
  • Illicit Drugs*
  • Longitudinal Studies
  • Male
  • Marijuana Abuse / epidemiology*
  • Marijuana Abuse / psychology
  • Matched-Pair Analysis*
  • Models, Statistical
  • Personality Assessment / statistics & numerical data
  • Poverty / psychology
  • Poverty / statistics & numerical data*
  • Prospective Studies
  • Psychometrics / statistics & numerical data*
  • Substance-Related Disorders / epidemiology*
  • Substance-Related Disorders / psychology
  • Unemployment / psychology
  • Unemployment / statistics & numerical data*

Substances

  • Illicit Drugs