RNN-based counterfactual prediction, with an application to homestead policy and public schooling
This addresses a domain-specific problem in policy evaluation for researchers and policymakers, but appears incremental as it applies a known method (RNNs) to a new application area.
The paper tackles the problem of estimating the long-run impact of U.S. homestead policy on public school spending by proposing an RNN-based method for counterfactual prediction, which learns temporal dependencies from control unit histories to predict outcomes, though no concrete numerical results are provided in the abstract.
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.