Combining the Regression Discontinuity Design and Propensity Score-based Weighting to Improve Causal Inference in Program Evaluation

The regression discontinuity (RD) design is considered to be the closest to a randomized trial that can be applied in non-experimental settings. The design relies on a cut-off point on a continuous baseline variable to assign individuals to treatment. The individuals just to the right and left of the cut-off are assumed to be exchangeable - as in a randomized trial. Any observed discontinuity in the relationship between the assignment variable and outcome is therefore considered evidence of a treatment effect. In this paper, we describe key advances in the RD design over the past decade and illustrate their implementation using data from a health management intervention. We then introduce the propensity score-based weighting technique as a complement to the RD design to correct for imbalances in baseline characteristics between treated and non-treated groups that may bias RD results. We find that the weighting strategy outperforms standard regression covariate adjustment in the present data. One clear advantage of the weighting technique over regression covariate adjustment is that we can directly inspect the degree to which balance was achieved. Because of its relative simplicity and tremendous utility, the RD design (either alone or combined with propensity score weighting adjustment) should be considered as an alternative approach to evaluate health management program effectiveness when using observational data.