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Uncertainty Quantification in Synthetic Controls with Staggered Treatment Adoption

Domain:

socioeconomic

Record type:

softwarepaper
Creator:
CatFenPalTit
Publisher:
arXiv
Host:avatar
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.

Visit

doi.orgarxiv.org

Tags

Econometrics (econ.EM)Applications (stat.AP)Computation (stat.CO)Methodology (stat.ME)FOS: Economics and businessFOS: Economics and businessFOS: Computer and information sciencesFOS: Computer and information sciences

Licenses

Creative Commons Attribution 4.0 Internationalhttps://creativecommons.org/licenses/by/4.0/legalcode

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