Review of methods for assessing the causal effect of binary interventions from aggregate time-series observational data

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

Research output: Contribution to journalArticle (Academic Journal)

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
JournalStatistical Science
Publication statusAccepted/In press - 19 May 2019

Keywords

  • intervention evaluation
  • panel data

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