Two analytical frameworks of regression discontinuity and Stata application

As one of the most important quasiexperimental causal inference methods, regression discontinuity design has two major analytical frameworks, which are quite different in terms of premise assumptions, bandwidth selection, and inference methods. Among them, the continuity-based framework assumes that the conditional expectation of the potential results is continuous and is widely used in empirical research. The local randomization framework is a rising star. This framework assumes that the driving variables can be regarded as randomly assigned in a small window near the breakpoint. This presentation will introduce the principles and techniques of these two frameworks, including identi