Inference Based on Imagined Randomization

Thomas M. Menino Convention & Exhibition Center 

Statistical analysis requires that something be random, e.g. random sampling from a population or randomized assignment of treatment. But often, it's unclear what, if anything, is actually random in the panels we're working with, so whatever statistical claims we make are based on a random mechanism we're imagining. This talk is about basing our analysis on imagined randomization of treatment: what assignment mechanisms we might want to imagine, what we can conclude if we buy into what we're imagining, and how we might interpret those conclusions with a little more skepticism. The main example will be an analysis of synthetic control methods having imagined that units select treatment independently with unknown unit-specific probabilities.