Addressing attribution of cause and effect in small n impact evaluations

Increased demand for results has resulted in a growth in large n impact evaluations involving tests of statistical significance between outcomes for treatment and comparison groups. However, this type of statistical analysis is not suitable for all evaluations. When there are insufficient units of assignment to conduct tests of statistical significance, then small n approaches are the best available option. However, while there is considerable consensus among large n researchers concerning what constitutes a valid approach, the same is not true for small n approaches. In fact, there is a plethora of possible approaches available to an evaluator, but little consensus concerning what approach, methodology, or methods are most suitable for an impact evaluation, and what constitutes valid causal evidence. This paper seeks to advance this discussion.
White & Phillips Small n Impact Evaluation WP Version,

White & Phillips Small n Impact Evaluation WP Version[1].