Abstract
Counterfactual analysis has a long and distinguished history in comparative research. To some, counterfactual analysis is central to comparative inquiry because such research typically embraces only a handful of empirical cases (Fearon 1991). If there are only a few instances (e.g., of revolution), then researchers, of necessity, must compare empirical cases to hypothetical cases. The affinity between counterfactual analysis and comparative research, however, derives not from its focus on small Ns, but from its configurational nature. Case-oriented explanations of outcomes are often combinatorial in nature, stressing specific configurations of causal conditions. Rather than focus on the net effects of causal conditions, case-oriented explanations emphasize their combined effects.
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Literatur
Winship and Morgan (1999: 660) argue that the language of „treatment“and „control“variables is generally applicable: „In almost any situation where a researcher attempts to estimate a causal effect, the analysis can be described, at least in terms of a thought experiment, as an experiment.“A more direct implication of using experimental language, which Winship and Morgan do not discuss in detail, is the restriction that „the treatment must be manipulable“(1999: 663, fn. 2). Citing Holland (1986), they argue that it makes no sense to talk about the causal effect of gender or any other nonmanipulable individual trait alone. One must explicitly model the manipulable mechanism that generates an apparent causal effect of a nonmanipulable attribute“(1999: 663, fn. 2).
In any event, counterfactual regression procedures have been developed for application to individual-level data and are feasible only when (1) there is a very large N, and (2) it is plausible a priori that each case could be in either the control or the treatment group (see Winship/ Morgan 1999). Also, these procedures, like conventional statistical analyses, remain linear and additive, so they do not examine problems of limited diversity and matched cases directly. An attempt to address limited diversity, or „the curse of dimensionality,“with Boolean logit and probit regression is offered by Braumoeller (2003).
This may or may not be the only pathway to having a generous welfare state. The focus here is simply on the evaluation of this pathway, with its four combined conditions.
There can be other, unspecified combinations of causal conditions linked to outcome Y in this example. There is no assumption that this is the only combination linked to the outcome (Y).
Note that methodological discussions of counterfactuals often assume a non-configurational variant of the „difficult“form, as in Fearon (1996: 39): „When trying to argue or assess whether some factor A caused event B, social scientists frequently use counterfactuals. That is, they either ask whether or claim that ‘if A had not occurred, B would not have occurred.’“
Note that Stokke (2004) includes condition A in his model, based on the recommendation in Ragin (2000: 105, 254) to perform necessary conditions tests prior to sufficiency tests. The counterfactual procedure described in this paper can be seen as an extension and reformulation of QCA techniques, one which locates the specification of necessary and sufficient conditions within a continuum of solutions defined by the most complex and the most parsimonious pathways.
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© 2005 VS Verlag für Sozialwissenschaften/GWV Fachverlage GmbH, Wiesbaden
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Ragin, C.C., Sonnett, J. (2005). Between Complexity and Parsimony: Limited Diversity, Counterfactual Cases, and Comparative Analysis. In: Kropp, S., Minkenberg, M. (eds) Vergleichen in der Politikwissenschaft. VS Verlag für Sozialwissenschaften. https://doi.org/10.1007/978-3-322-80441-9_9
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DOI: https://doi.org/10.1007/978-3-322-80441-9_9
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