  This paper studies a panel data setting where the goal is to estimate causal
effects of an intervention by predicting the counterfactual values of outcomes
for treated units, had they not received the treatment. Several approaches have
been proposed for this problem, including regression methods, synthetic control
methods and matrix completion methods. This paper considers an ensemble
approach, and shows that it performs better than any of the individual methods
in several economic datasets. Matrix completion methods are often given the
most weight by the ensemble, but this clearly depends on the setting. We argue
that ensemble methods present a fruitful direction for further research in the
causal panel data setting.
