  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 different approaches and provide guidelines regarding
their practical implementation. Several open problems are identified thus
highlighting the need for future research.
