Prediction Intervals for Synthetic Control Methods

Uncertainty quantification is a fundamental problem in the analysis and interpretation of synthetic control (SC) methods. We develop conditional prediction intervals in the SC framework, and provide conditions under which these intervals offer finite-sample probability guarantees. Our method allows...

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Bibliographic Details
Published in:Journal of the American Statistical Association Vol. 116; no. 536; pp. 1865 - 1880
Main Authors: Cattaneo, Matias D., Feng, Yingjie, Titiunik, Rocio
Format: Journal Article
Language:English
Published: Alexandria Taylor & Francis 02.10.2021
Taylor & Francis Ltd
Subjects:
Causal inference
computer software
Control methods
Empirical analysis
Intervals
Measurement
Nonasymptotic inference
Numerical methods
prediction
Prediction intervals
Predictions
Probability
Randomness
Regression analysis
Sensitivity analysis
Simulation
Statistical analysis
Statistical methods
Statistics
Synthetic controls
Uncertainty
ISSN:0162-1459, 1537-274X, 1537-274X
Online Access:Get full text
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Summary:Uncertainty quantification is a fundamental problem in the analysis and interpretation of synthetic control (SC) methods. We develop conditional prediction intervals in the SC framework, and provide conditions under which these intervals offer finite-sample probability guarantees. Our method allows for covariate adjustment and nonstationary data. The construction begins by noting that the statistical uncertainty of the SC prediction is governed by two distinct sources of randomness: one coming from the construction of the (likely misspecified) SC weights in the pretreatment period, and the other coming from the unobservable stochastic error in the post-treatment period when the treatment effect is analyzed. Accordingly, our proposed prediction intervals are constructed taking into account both sources of randomness. For implementation, we propose a simulation-based approach along with finite-sample-based probability bound arguments, naturally leading to principled sensitivity analysis methods. We illustrate the numerical performance of our methods using empirical applications and a small simulation study. Python, R and Stata software packages implementing our methodology are available. Supplementary materials for this article are available online.
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ISSN:0162-1459
1537-274X
1537-274X
DOI:10.1080/01621459.2021.1979561