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RNN‐based counterfactual prediction, with an application to homestead policy and public schooling

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  • Jason Poulos
  • Shuxi Zeng

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 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.

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  • Jason Poulos & Shuxi Zeng, 2021. "RNN‐based counterfactual prediction, with an application to homestead policy and public schooling," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(4), pages 1124-1139, August.
    • Handle: RePEc:bla:jorssc:v:70:y:2021:i:4:p:1124-1139
    DOI: 10.1111/rssc.12511
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    Cited by:

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    2. Amit Yaniv-Rosenfeld & Elizaveta Savchenko & Ariel Rosenfeld & Teddy Lazebnik, 2023. "Scheduling BCG and IL-2 Injections for Bladder Cancer Immunotherapy Treatment," Mathematics, MDPI, vol. 11(5), pages 1-13, February.

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