The December 2008 issue of Psychological Methods includes an article by Joseph Schafer and Joseph Kang, entitled, "Average Causal Effects From Nonrandomized Studies: A Practical Guide and Simulated Example" (abstract).
The article addresses the seemingly age-old issue of how best to approximate a causal inference when participants' levels of the purported "causal" variable have not been assigned at random. Although the article contains a fair amount of jargon and technical formulas, the key foundation appears to boil down to the following quote:
In a typical observational study... it is unlikely that [treatment condition] will be independent of [individuals' outcome scores]. The treatments may have been selected by the individuals themselves, for reasons that are possibly related to the outcomes. With observational data, a good estimate of the [average causal effect] will make use of the covariates... to help account for this dependence (p. 281).
Via a large simulation study, Schafer and Kang compare nine different approaches for covariate-based adjustment, including analysis of covariance (ANCOVA), regression, matching, propensity scores, and weighting schemes. Near the end of the article, the authors present a section entitled "Lessons Learned," containing practical recommendations.
As social-science research articles continue to use increasingly sophisticated statistical and analytical methods, Schafer and Kang's article should be a useful resource for researchers looking to remain current with state-of-the-art approaches.