A Note on Doubly Robust Estimator in Regression Discontinuity Designs
This work addresses robustness issues in causal inference for researchers using regression discontinuity designs, though it is incremental as it builds on existing doubly robust methods.
The paper tackles the problem of ensuring consistent treatment effect estimation in regression discontinuity designs by proposing a doubly robust estimator that remains consistent if at least one of two underlying estimators is correct, enhancing robustness in quasi-experimental settings.
This note introduces a doubly robust (DR) estimator for regression discontinuity (RD) designs. RD designs provide a quasi-experimental framework for estimating treatment effects, where treatment assignment depends on whether a running variable surpasses a predefined cutoff. A common approach in RD estimation is the use of nonparametric regression methods, such as local linear regression. However, the validity of these methods still relies on the consistency of the nonparametric estimators. In this study, we propose the DR-RD estimator, which combines two distinct estimators for the conditional expected outcomes. The primary advantage of the DR-RD estimator lies in its ability to ensure the consistency of the treatment effect estimation as long as at least one of the two estimators is consistent. Consequently, our DR-RD estimator enhances robustness of treatment effect estimators in RD designs.