  This paper proposes a method for estimating the effect of a policy
intervention on an outcome over time. We train recurrent neural networks (RNNs)
on the history of control unit outcomes to learn a useful representation for
predicting future outcomes. The learned representation of control units is then
applied to the treated units for predicting counterfactual outcomes. RNNs are
specifically structured to exploit temporal dependencies in panel data, and are
able to learn negative and nonlinear interactions between control unit
outcomes. We apply the method to the problem of estimating the long-run impact
of U.S. homestead policy on public school spending.
