Non-randomized studies of treatment effects have come under criticism because of their failure to control for potential biases introduced by unobserved variables correlated with treatment selection and outcomes. This paper describes the basic concepts of sample selection models--a technique used widely in the economics evaluation literature for nearly two decades--and discusses the potential role of these models in outcomes research. In addition, it presents a case study of the application of the sample selection modelling approach to evaluation of the effects of antidepressant therapies on medical expenditures for physician services. This case study presents empirical comparisons of alternative model specifications and discusses practical issues in evaluation of sample selection models. We demonstrate that, in this particular case, sample selection models yield very different conclusions regarding treatment effects than traditional ordinary least squares regression.