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.
|Publication status||Accepted/In press - 19 May 2019|
- intervention evaluation
- panel data
Samartsidis, P., Seaman, S. R., Presanis, A. M., Hickman, M., & De Angelis, D. (Accepted/In press). Review of methods for assessing the causal effect of binary interventions from aggregate time-series observational data. Statistical Science.