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.
Yet, given these insights from modern statistics, how can social scientists say anything causal at all? Counterfactuals are, in fact, metaphysical constructions that are intuitively accessible to most people. To put it another way, we have evolved to think counterfactually for survival. For example, if a tiger is chasing me, I construct instantaneously a counterfactual of me climbing a tree versus not climbing a tree based on a wide range of information and life experiences, and I decide to climb the tree. This kind of counterfactual thinking is not based on a set of systematized principles (as in statistics), but it is useful, reliable, and based on hunches, insights, and inbuilt cognitive processes. (However, Kahenman and Miller have presented evidence that people engage in counterfactual more often with extreme or unusual events.) Moreover, this kind of counterfactual thinking is the basis for modern science: actual social science progresses based on thick (or thicker) description and theoretical counterfactuals based on a wide range of information and studies, exactly what ethnographers have been doing for over 100 years.
