  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, and
valid with stationary and non-stationary data. It demonstrates an excellent
small sample performance in application-based simulations and performs well
relative to alternative methods. We illustrate the usefulness of the $t$-test
by revisiting the effect of carbon taxes on emissions.
