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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 smallarXiv:1812.10820v7 fatcat:hjt3qhgqjvadhhxynihacldlpq