This article provides a detailed discussion of the theory and practice of modern regression discontinuity (RD) analysis for estimating the effects of interventions or treatments. Part 1 briefly chronicles the history of RD analysis and summarizes its past applications. Part 2 explains how in theory an RD analysis can identify an average effect of treatment for a population and how different types of RD analyses--"sharp" versus "fuzzy"--can identify average treatment effects for different conceptual subpopulations. Part 3 of the article introduces graphical methods, parametric statistical methods, and nonparametric statistical methods for estimating treatment effects in practice from regression discontinuity data plus validation tests and robustness tests for assessing these estimates. Section 4 considers generalizing RD findings and presents several different views on and approaches to the issue. Part 5 notes some important issues to pursue in future research about or applications of RD analysis. (Contains 11 figures, 1 table, and 24 footnotes.)