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 synthetic control and many other 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  .
We propose a practical and robust method for making inferences on average treatment effects estimated by synthetic controls . 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 t-test is easy to implement, provably robust against misspecification, valid with non-stationary data, and demonstrates an excellent small sample performance . Compared to difference-in-differences, our method often yields more than 50\% shorter confidence intervals and is robust to violations of parallel trends assumptions. An R-package for implementing our methods is available .
R
This paper proposes
We propose
R
inference
inferences
R
control and related methods
controls
R
procedure only requires consistent (in \ell_2-norm) estimation of the parameters, which can be verified for synthetic control and many other popular estimators. The proposed method
t-test
D
more efficient than difference-in-differences,
A
. Compared to difference-in-differences, our method often yields more than 50\% shorter confidence intervals and is robust to violations of parallel trends assumptions. An R-package for implementing our methods is available