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 non-linear interactions between control unit outcomes. We apply the method to the problem of estimating the long-run impact of US homestead policy on public school spending.

中文翻译：

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基于 RNN 的反事实预测，应用于宅基地政策和公立学校教育

本文提出了一种评估政策干预对结果的影响的方法。我们在控制单元结果的历史上训练循环神经网络 (RNN)，以学习预测未来结果的有用表示。然后将学习到的控制单元表示应用于处理单元以预测反事实结果。RNN 专门用于利用面板数据中的时间依赖性，并且能够学习控制单元结果之间的负面和非线性相互作用。我们将该方法应用于估计美国宅基地政策对公立学校支出的长期影响的问题。