Jacob Goldin, Julian Nyarko, Justin Young
arXiv 6 Aug 2022 · Econometrics · 1 citations (OpenAlex)
arXiv:2208.03489 · PDF · DOI · OpenAlex · Extracted main text
Conducting causal inference with panel data is a core challenge in social science research. We adapt a deep neural architecture for time series forecasting (the N-BEATS algorithm) to more accurately impute the counterfactual evolution of a treated unit had treatment not occurred. Across a range of settings, the resulting estimator (“SyNBEATS”) significantly outperforms commonly employed methods (synthetic controls, two-way fixed effects), and attains comparable or more accurate performance compared to recently proposed methods (synthetic difference-in-differences, matrix completion). An implementation of this estimator is available for public use. Our results highlight how advances in the forecasting literature can be harnessed to improve causal inference in panel data settings.
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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.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Viviano and Bradic (2023) Synthetic Learner: Model-free inference on treatments over time | 1.000 | 5 | 3 | 100% |
| 2 | Abadie, Diamond and Hainmueller (2010) Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California’s Tobacco Control Program | 0.956 | 8 | 6 | 88% |
| 3 | Oreshkin, Carpov, Chapados and Bengio (2019) N-BEATS: Neural basis expansion analysis for interpretable time series forecasting | 0.928 | 4 | 4 | 100% |
| 4 | Athey, Bayati, Doudchenko, Imbens and Khosravi (2021) Matrix Completion Methods for Causal Panel Data Models | 0.928 | 4 | 3 | 100% |
| 5 | Arkhangelsky, Athey, Hirshberg, Imbens and Wager (2021) Synthetic Difference-in-Differences | 0.843 | 3 | 3 | 100% |
| 6 | Baker and Gelbach (2020) | 0.737 | 3 | 2 | 100% |
| 7 | Olivares, Challu, Marcjasz, Weron and Dubrawski (2021) Neural basis expansion analysis with exogenous variables: Forecasting electricity prices with NBEATSx | 0.737 | 3 | 2 | 100% |
| 8 | Abadie, Diamond and Hainmueller (2015) Comparative Politics and the Synthetic Control Method | 0.644 | 2 | 2 | 100% |
| 9 | Doudchenko and Imbens (2016) Balancing, Regression, Difference-In-Differences and Synthetic Control Methods: A Synthesis | 0.644 | 2 | 2 | 100% |
| 10 | Chernozhukov, Wuthrich and Zhu (2018) A $t$-test for synthetic controls | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 24 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Causal Forecasting in Panel Data: A Two-Way Synthetic Forecasting Approach | 0.405 | 1 | 1 |