Augusto Cerqua, Marco Letta, Fiammetta Menchetti
arXiv 10 Dec 2023 · Econometrics · 5 citations (OpenAlex)
arXiv:2312.05858 · PDF · DOI · OpenAlex · Extracted main text
Without a control group, the most widespread methodologies for estimating causal effects cannot be applied. To fill this gap, we propose the Machine Learning Control Method, a new approach for causal panel analysis that estimates causal parameters without relying on untreated units. We formalize identification within the potential outcomes framework and then provide estimation based on machine learning algorithms. To illustrate the practical relevance of our method, we present simulation evidence, a replication study, and an empirical application on the impact of the COVID-19 crisis on educational inequality. We implement the proposed approach in the companion R package MachineControl
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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 | Arkhangelsky, D. and G. Imbens (2024) Causal models for longitudinal and panel data: A survey | 0.928 | 4 | 3 | 100% |
| 2 | Imbens, G. W. and D. B. Rubin (2015) Causal inference in Statistics, Social, and Biomedical Sciences | 0.811 | 4 | 2 | 100% |
| 3 | Carlana, M., E. La Ferrara, and C. Lopez (2023) Exacerbated inequalities: The learning loss from covid-19 in italy | 0.737 | 4 | 2 | 75% |
| 4 | Viviano, D. and J. Bradic (2023) Synthetic learner: model-free inference on treatments over time | 0.737 | 3 | 3 | 67% |
| 5 | Carvalho, C., R. Masini, and M. C. Medeiros (2018) ArCo: An artificial counterfactual approach for high-dimensional panel time-series data | 0.737 | 3 | 2 | 100% |
| 6 | Masini, R. and M. C. Medeiros (2021) Counterfactual analysis with artificial controls: Inference, high dimensions, and nonstationarity | 0.737 | 3 | 2 | 100% |
| 7 | Wager, S. and S. Athey (2018) Estimation and inference of heterogeneous treatment effects using random forests | 0.737 | 3 | 2 | 100% |
| 8 | Baltagi, B. H (2008) Econometric analysis of panel data, Volume 4 | 0.737 | 3 | 2 | 100% |
| 9 | Abadie, A., A. Diamond, and J. Hainmueller (2010) Synthetic control methods for comparative case studies: Estimating the effect of California’s tobacco control program | 0.644 | 2 | 2 | 100% |
| 10 | Brodersen, K. H., F. Gallusser, J. Koehler, N. Remy, and S. L. Scott (2015) Inferring causal impact using bayesian structural time-series models | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 74 scored citations.