Uncertainty Quantification in Synthetic Controls with Staggered Treatment Adoption
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2022
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| _version_ | 1866915131749826560 |
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| author | Cattaneo, Matias D. Feng, Yingjie Palomba, Filippo Titiunik, Rocio |
| author_facet | Cattaneo, Matias D. Feng, Yingjie Palomba, Filippo Titiunik, Rocio |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2210_05026 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Uncertainty Quantification in Synthetic Controls with Staggered Treatment Adoption Cattaneo, Matias D. Feng, Yingjie Palomba, Filippo Titiunik, Rocio Econometrics Applications Computation Methodology 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. |
| title | Uncertainty Quantification in Synthetic Controls with Staggered Treatment Adoption |
| topic | Econometrics Applications Computation Methodology |
| url | https://arxiv.org/abs/2210.05026 |