  Policy researchers using synthetic control methods typically choose a donor
pool in part by using policy domain expertise so the untreated units are most
like the treated unit in the pre intervention period. This potentially leaves
estimation open to biases, especially when researchers have many potential
donors. We compare how functional principal component analysis synthetic
control, forward-selection, and the original synthetic control method select
donors. To do this, we use Gaussian Process simulations as well as policy case
studies from West German Reunification, a hotel moratorium in Barcelona, and a
sugar-sweetened beverage tax in San Francisco. We then summarize the
implications for policy research and provide avenues for future work.
