Counterfactual Analysis With Artificial Controls: Inference, High Dimensions, and Nonstationarity

Ricardo Masini, Marcelo C. Medeiros

Research output: Contribution to journalArticlepeer-review

9 Scopus citations


Recently, there has been growing interest in developing statistical tools to conduct counterfactual analysis with aggregate data when a single “treated” unit suffers an intervention, such as a policy change, and there is no obvious control group. Usually, the proposed methods are based on the construction of an artificial counterfactual from a pool of “untre ated” peers, organized in a panel data structure. In this article, we consider a general framework for counterfactual analysis for high-dimensional, nonstationary data with either deterministic and/or stochastic trends, which nests well-established methods, such as the synthetic control. We propose a resampling procedure to test intervention effects that does not rely on postintervention asymptotics and that can be used even if there is only a single observation after the intervention. A simulation study is provided as well as an empirical application. Supplementary materials for this article are available online.

Original languageEnglish (US)
Pages (from-to)1773-1788
Number of pages16
JournalJournal of the American Statistical Association
Issue number536
StatePublished - 2021

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty


  • Cointegration
  • Comparative studies
  • Intervention
  • Policy evaluation
  • Resampling
  • Synthetic control
  • panel data


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