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

Jason Poulos, Shuxi Zeng

arXiv 10 Dec 2017 · Statistics — Machine Learning · 1 citations (OpenAlex)

arXiv:1712.03553 · PDF · DOI · OpenAlex · Extracted main text

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.

Citation extraction

67
references
73
in-text mentions
67
distinct cited
0
self-citations
6,509
main-text words

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Most heavily cited references

The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.

ReferenceIntensityMentionsSectionsMain text
1Goel, H., Melnyk, I. and Banerjee, A. (2017) R2N2: Residual Recurren… arXiv e-prints, arXiv:1709.031590.64422100%
2Athey, S., Bayati, M., Doudchenko, N., Imbens, G. and Khosravi, K. (… (2017) arXiv e-prints, arXiv:1710.102510.64422100%
3Bahdanau, D., Cho, K. and Bengio, Y. (2014) Neural Machine Translati… arXiv e-prints, arXiv:1409.04730.51121100%
4Brodersen, K. H., Gallusser, F., Koehler, J., Remy, N. and Scott, S.… (2015) The Annals of Applied Statistics, 9, 247–2740.51121100%
5Doudchenko, N. and Imbens, G. W. (2016) Balancing, regression, diffe… arXiv e-prints, arXiv:1610.077480.51121100%
6Haines, M. R. (2010) Historical, Demographic, Economic, and Social D… Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2010-05-21. doi.org/10.3886/ICPSR028…0.51121100%
7Chernozhukov, V., Chetverikov, D., Demirer, M., Duflo, E., Hansen, C… (2018) The Econometrics Journal, 21, C1–C680.40511100%
8Ben-Michael, E., Feller, A. and Rothstein, J. (2018) The Augmented S… arXiv e-prints, arXiv:1811.041700.40511100%
9General Land Office, 2017 (2017) General Land Office (GLO) Records A… Bureau of Land Management, Washington, DC0.40511100%
10Bennett, A., Kallus, N. and Schnabel, T. (2019) Deep generalized met… In Advances in Neural Information Processing Systems (eds. H. Wallach, H. Larochelle, A. Beygelzimer, F. d Alché-Buc, E. Fox and…0.40511100%

Showing the top 10 of 67 scored citations.

Cited by, within the corpus

arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.

Citing paperIntensityMentionsSections
1State-Building through Public Land Disposal? An Application of Matrix Completion for Counterfactual Prediction0.40511
2Forecasting Algorithms for Causal \ Inference with Panel Data0.40511