Elevator pitch
It is common practice to incorporate additional covariates in empirical economics. In the context of regression discontinuity (RD) designs, covariate adjustment plays multiple roles, making it essential to understand its impact on analysis and conclusions. Typically implemented via local least squares regressions, covariate adjustment can serve three main distinct purposes: (i) improving the efficiency of RD average causal effect estimators, (ii) learning about heterogeneous RD policy effects, and (iii) changing the RD parameter of interest.
Key findings
Pros
Precision of RD causal effect estimators can be improved by correctly using pre-intervention covariates.
When properly implemented, covariate adjustment can uncover interesting heterogeneous RD causal effects.
Various other RD policy effects of interest can be learned by correctly incorporating co-variates in the analysis.
Cons
Understanding empirical results based on covariate-adjusted RD estimates requires careful consideration.
Covariate adjustment cannot restore the validity of an RD design without strong parametric assumptions.
Empirical work leveraging covariates in RD designs is often undisciplined and ad hoc, potentially leading to invalid empirical findings and policy prescriptions.
