Matching methods for causal inference: Designing observational studies

Much research in the social sciences attempts to estimate the effect of some intervention or “treatment” such as a school dropout prevention program or television watching. However, particularly in the social sciences, it is generally not possible to randomly assign units to receive the treatment condition or the control condition, and thus the resulting data are observational, where we simply observe that some units received the treatment and others did not. In such cases, there is a need to control for differences in the covariate distributions between the treatment and control groups. Matching methods, such as propensity score matching, effect this control by selecting subsets of the treatment and control groups with similar covariate distributions. The overall theme is of replicating a randomized experiment in two ways: first, by comparing treated and control units who look as if they could have been randomly assigned to treatment or control status; and second, by forming the comparison groups without the use of the outcome, thus preventing intentional or unintentional bias in selecting a particular sample to achieve a desired result. This chapter focuses on how to design observational studies using matching methods and the related ideas of subclassification and weighting. We present practical guidance regarding the use of matching methods, as well as examples of their use and evidence of their improved performance relative to other methods of controlling for bias due to observed covariates.