This paper proposes a practical and robust method for making inference on average treatment effects estimated by synthetic control and related methods. We develop a K-fold cross-fitting procedure for bias-correction. To avoid the difficult estimation of the long-run variance, inference is based on a self-normalized t-test statistic , which has an asymptotically pivotal t-distribution. Our procedure only requires consistent (in \ell_2-norm) estimation of the parameters, which can be verified for many popular estimators. The proposed method is easy to implement, provably robust against misspecification  , and demonstrates an excellent small sample performance.
This paper proposes a practical and robust method for making inference on average treatment effects estimated by synthetic control and related methods. We develop a K-fold cross-fitting procedure for bias-correction. To avoid the difficult estimation of the long-run variance, inference is based on a self-normalized t-statistic , which has an asymptotically pivotal t-distribution. Our procedure only requires consistent (in \ell_2-norm) estimation of the parameters, which can be verified for many popular estimators. The proposed method is easy to implement, provably robust against misspecification , more efficient than difference-in-differences, valid with non-stationary data , and demonstrates an excellent small sample performance.
R
t-test statistic
t-statistic
A
, more efficient than difference-in-differences, valid with non-stationary data