Prediction Intervals for Synthetic Control Methods

Matias D. Cattaneo, Yingjie Feng, Rocio Titiunik

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)1865-1880
Number of pages16
JournalJournal of the American Statistical Association
Volume116
Issue number536
DOIs
StatePublished - 2021

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

Keywords

  • Causal inference
  • Nonasymptotic inference
  • Prediction intervals
  • Synthetic controls

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