Causality and Ethnography

The University of Chicago is hosting a conference on causality and ethnography on March 8th and 9th. Full details are available here. My own view on the relationship between causality and ethnography is that ethnographers should use counterfactuals, and in fact usually do whether or not they are explicit about them. In modern statistics (in particular, the work of Donald Rubin at Harvard, among others, on the potential outcomes model), the counterfactual model of causaltiy clarifies the conditions under which any particular data set can be interpreted as causal, and shows that these assumptions are extremely strong. Contra the prevailing view of many economists, even instrumental variables regression, regression discontinuity design, and related methods require exceptionally (and often implausibly) strong assumptions for causal interpretation.