Nearly two years ago, we first mentioned propensity scores as a tool for trying to draw as strong a causal inference as possible from nonexperimental designs, between exposure to a "treatment" experience and some later outcome. For example, how might attending private (vs. public) schools affect students' intellectual curiosity? An article by Harder, Stuart, and Anthony in the September 2010 Psychological Methods offers practical advice and concrete examples for conducting propensity-score analyses. Propensity scores involve matching the treatment and control groups on other variables (covariates) that are thought to predict treatment status or have "potential to confound the relationship between the treatment and the outcome" (p. 235). Harder et al. discuss several specific methods for implementing such matching, such as 1:1, 1:k (where one treated participant can be matched to multiple comparison persons), and full matching (where one matched set can include multiple treated individuals and multiple comparison counterparts). The article also discusses software packages for implementing propensity scores, although these do not appear very plentiful at the moment. The article is definitely worth a look by anyone contemplating a correlational or quasi-experimental study that one hopes to frame in causal terms (acknowledging that a full causal claim will be out of reach with such designs).