Assessing the Causal Effect of Binary Interventions from Observational Panel Data with Few Treated Units

Pantelis Samartsidis, Shaun R. Seaman, Anne M Presanis, Matthew Hickman, Daniela De Angelis

Research output: Contribution to journalArticle (Academic Journal)peer-review

20 Citations (Scopus)
110 Downloads (Pure)

Abstract

Researchers are often challenged with assessing the impact of an intervention on an outcome of interest in situations where the intervention is non-randomised, the intervention is only applied to one or few units, the intervention is binary, and outcome measurements are available at multiple time points. In this paper, we review existing methods for causal inference in these situations.We detail the assumptions underlying each method, emphasize connections between the dierent approaches and provide guidelines regarding their practical implementation. Several open problems are identied thus highlighting the need for future research.
Original languageEnglish
Pages (from-to)486 - 503
Number of pages18
JournalStatistical Science
Volume34
Issue number3
DOIs
Publication statusPublished - 1 Aug 2019

Keywords

  • Causal impact
  • causal inference
  • difference-indifferences
  • intervention evaluation
  • latent factor models
  • panel data
  • synthetic controls

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