Skip to content

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

Research output: Contribution to journalArticle

Original languageEnglish
JournalStatistical Science
DateAccepted/In press - 19 May 2019


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.

    Research areas

  • intervention evaluation, panel data



  • Full-text PDF (accepted author manuscript)

    Accepted author manuscript, 459 KB, PDF document

    Embargo ends: 1/01/99

    Request copy

View research connections

Related faculties, schools or groups