Review of methods for assessing the causal effect of binary interventions from aggregate time-series observational data
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Researchers are often interested in assessing the impact of an intervention on an outcome of interest in situations where the intervention is non-randomised, information is available at an aggregate level, the intervention is only applied to one or few units, the intervention is binary, and there are outcome measurements at multiple time points. In this paper, we review existing methods for causal inference in the setup just outlined. We detail the assumptions underlying each method, emphasise connections between the different approaches and provide guidelines regarding their practical implementation. Several open problems are identified thus highlighting the need for future research.
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