  Understanding the effect of a particular treatment or a policy pertains to
many areas of interest, ranging from political economics, marketing to
healthcare. In this paper, we develop a non-parametric algorithm for detecting
the effects of treatment over time in the context of Synthetic Controls. The
method builds on counterfactual predictions from many algorithms without
necessarily assuming that the algorithms correctly capture the model. We
introduce an inferential procedure for detecting treatment effects and show
that the testing procedure is asymptotically valid for stationary, beta mixing
processes without imposing any restriction on the set of base algorithms under
consideration. We discuss consistency guarantees for average treatment effect
estimates and derive regret bounds for the proposed methodology. The class of
algorithms may include Random Forest, Lasso, or any other machine-learning
estimator. Numerical studies and an application illustrate the advantages of
the method.
