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The article explores the role that subjective evidence of causality and associated counterfactuals and counterpotentials might play in the social sciences where comparative cases are scarce. This scarcity rules out statistical inference based upon frequencies and usually invites in-depth ethnographic studies. Thus, if causality is to be preserved in such situations, a conception of ethnographic causal inference is required. Ethnographic causality inverts the standard statistical concept of causal explanation in observational studies, whereby comparison and generalization, across a sample of cases, are both necessary prerequisites for any causal inference. Ethnographic causality allows, in contrast, for causal explanation prior to any subsequent comparison or generalization. [ABSTRACT FROM AUTHOR]

We delineate the underlying homogeneity assumption, procedural variants, and implications of the comparative method and distinguish this from Mill’s method of difference. We demonstrate that additional units can provide “placebo” tests for the comparative method even if the scope of inference is limited to the two units under comparison. Moreover, such tests may be available even when these units are the most similar pair of units on the control variables with differing values of the independent variable. Small-n analyses using this method should therefore, at a minimum, clearly define the dependent, independent, and control variables so they may be measured for additional units, and specify how the control variables are weighted in defining similarity between units. When these tasks are too difficult, process tracing of a single unit may be a more appropriate method. We illustrate these points with applications to two studies. [ABSTRACT FROM AUTHOR]

The increasing availability of data from multisite randomized trials provides a potential opportunity to use instrumental variables (IV) methods to study the effects of multiple hypothesized mediators of the effect of a treatment. We derive nine assumptions needed to identify the effects of multiple mediators when using site-by-treatment interactions to generate multiple instruments. Three of these assumptions are unique to the multiple-site, multiple-mediator case: (1) the assumption that the mediators act in parallel (no mediator affects another mediator); (2) the assumption that the site-average effect of the treatment on each mediator is independent of the site-average effect of each mediator on the outcome; and (3) the assumption that the site-by-compliance matrix has sufficient rank. The first two of these assumptions are nontrivial and cannot be empirically verified, suggesting that multiple-site, multiple-mediator IV models must be justified by strong theory. [ABSTRACT FROM PUBLISHER]