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Researchers are often challenged with assessing the impact of an interventionon an outcome of interest in situations where the intervention isnon-randomised, the intervention is only applied to one or few units, theintervention is binary, and outcome measurements are available at multiple timepoints. In this paper, we review existing methods for causal inference in thesesituations. We detail the assumptions underlying each method, emphasizeconnections between the different approaches and provide guidelines regardingtheir practical implementation. Several open problems are identified thushighlighting the need for future research.

Assessing the causal effect of binary interventions from observational panel data with few treated units