Regression discontinuity design is an alternative to randomized experiments to make causal inference when random assignment is not possible. This article first presents the formal identification and estimation of regression discontinuity treatment effects in the framework of Rubin's causal model, followed by a thorough literature review of three major methods for estimating regression discontinuity effects. The authors conducted a Monte Carlo simulation to compare the accuracy of 3 estimation methods and evaluate the effects of sample sizes, cutoff score locations, and distribution assumptions on the accuracy of parameter estimates. Although all 3 methods can produce reasonably accurate parameter estimates under various manipulated data conditions, extreme cutoff scores tend to introduce large parameter estimate biases when the variance of the outcome variable differs across groups. Implications and directions for further research are discussed. (Contains 3 figures and 3 tables.)