Matias D. Cattaneo, Yingjie Feng, Filippo Palomba, Rocio Titiunik
arXiv 10 Oct 2022 · Econometrics · publishedThe Review of Economics and Statistics (2025) · 8 citations (OpenAlex)
arXiv:2210.05026 · PDF · DOI · OpenAlex · Extracted main text
We propose principled prediction intervals to quantify the uncertainty of a large class of synthetic control predictions (or estimators) in settings with staggered treatment adoption, offering precise non-asymptotic coverage probability guarantees. From a methodological perspective, we provide a detailed discussion of different causal quantities to be predicted, which we call causal predictands, allowing for multiple treated units with treatment adoption at possibly different points in time. From a theoretical perspective, our uncertainty quantification methods improve on prior literature by (i) covering a large class of causal predictands in staggered adoption settings, (ii) allowing for synthetic control methods with possibly nonlinear constraints, (iii) proposing scalable robust conic optimization methods and principled data-driven tuning parameter selection, and (iv) offering valid uniform inference across post-treatment periods. We illustrate our methodology with an empirical application studying the effects of economic liberalization on real GDP per capita for Sub-Saharan African countries. Companion software packages are provided in Python, R, and Stata.
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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 | Cattaneo, M. D., Feng, Y., and Titiunik, R (2021) Prediction Intervals for Synthetic Control Methods self | 0.874 | 6 | 2 | 100% |
| 2 | Cattaneo, M. D., Feng, Y., Palomba, F., and Titiunik, R (2025) scpi: Uncertainty Quantification for Synthetic Control Methods self | 0.644 | 2 | 2 | 100% |
| 3 | Boyd, S., and Vandenberghe, L (2004) Convex Optimization | 0.511 | 2 | 1 | 100% |
| 4 | Raic, M (2019) A Multivariate Berry–Esseen Theorem with Explicit Constants | 0.511 | 2 | 1 | 100% |
| 5 | Billmeier, A., and Nannicini, T (2013) Assessing Economic Liberalization Episodes: A Synthetic Control Approach | 0.405 | 1 | 1 | 100% |
| 6 | Bratton, M., and Van de Walle, N (1997) Democratic Experiments in Africa: Regime Transitions in Comparative Perspective | 0.405 | 1 | 1 | 100% |
| 7 | Chang, Y., Park, J. Y., and Song, K (2006) Bootstrapping Cointegrating Regressions | 0.405 | 1 | 1 | 100% |
| 8 | Ferman, B., and Pinto, C (2021) Synthetic Controls with Imperfect Pre-Treatment Fit | 0.405 | 1 | 1 | 100% |
| 9 | Hoerl, A. E., Kannard, R. W., and Baldwin, K. F (1975) Ridge Regression: Some Simulations | 0.405 | 1 | 1 | 100% |
| 10 | Ravishanker, N., Hochberg, Y., and Melnick, E. L (1987) Approximate Simultaneous Prediction Intervals for Multiple Forecasts | 0.405 | 1 | 1 | 100% |
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