  In this paper the regression discontinuity design is adapted to the survival
analysis setting with right-censored data, studied in an intensity based
counting process framework. In particular, a local polynomial regression
version of the Aalen additive hazards estimator is introduced as an estimator
of the difference between two covariate dependent cumulative hazard rate
functions. Large-sample theory for this estimator is developed, including
confidence intervals that take into account the uncertainty associated with
bias correction. As is standard in the causality literature, the models and the
theory are embedded in the potential outcomes framework. Two general results
concerning potential outcomes and the multiplicative hazards model for survival
data are presented.
