  Many macroeconomic policy questions may be assessed in a case study
framework, where the time series of a treated unit is compared to a
counterfactual constructed from a large pool of control units. I provide a
general framework for this setting, tailored to predict the counterfactual by
minimizing a tradeoff between underfitting (bias) and overfitting (variance).
The framework nests recently proposed structural and reduced form machine
learning approaches as special cases. Furthermore, difference-in-differences
with matching and the original synthetic control are restrictive cases of the
framework, in general not minimizing the bias-variance objective. Using
simulation studies I find that machine learning methods outperform traditional
methods when the number of potential controls is large or the treated unit is
substantially different from the controls. Equipped with a toolbox of
approaches, I revisit a study on the effect of economic liberalisation on
economic growth. I find effects for several countries where no effect was found
in the original study. Furthermore, I inspect how a systematically important
bank respond to increasing capital requirements by using a large pool of banks
to estimate the counterfactual. Finally, I assess the effect of a changing
product price on product sales using a novel scanner dataset.
