RNN‐based counterfactual prediction, with an application to homestead policy and public schooling. Issue 4 (26th June 2021)

• Record Type: Journal Article
• Authors: Poulos, Jason; Zeng, Shuxi
• Is Part Of: Journal of the Royal Statistical Society. Volume 70:Issue 4(2021)
• Page Start: 1124
• Page End: 1139
• Publication Date: 2021-06-26
• Subjects: counterfactual prediction -- panel data -- political economy -- recurrent neural networks -- synthetic controls; Statistics -- Periodicals; 519.5
• DOI: 10.1111/rssc.12511
• Languages: English
• ISSNs: 0035-9254

Abstract

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.