A theoretically grounded approach to causal questions illuminates both the utility and limitations of the potential outcomes (PO) framework as a model for historically-focused, quantitative empirical research. While some causal questions are immediately reconcilable with the PO framework, for others, theoretical guidance is valuable in ascertaining relevant comparisons or characterizing the generalizability of findings to different contexts. A third category of important causal relationships feature strategic or information-based interactions, or multiple or unobservable mechanisms, many of which cannot be directly tested using the PO framework. Here, theory is critical in elucidating additional, observable implications that may be tested empirically. In all three categories, historical research promises special benefits: it expands the set of cases on which to test causal claims, may provide counterfactuals not available in contemporary contexts, and can feature institutional transformations that function as plausibly exogenous modifier variables. We clarify this classification of causal questions using examples from our own historical research.
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Below, we consider an example of research that exploits historical data for precisely this purpose.
That practitioners in the PO tradition often do not explicitly state their assumptions concerning operative mechanisms lies at the heart Pearl’s 2009 critique of the framework. Pearl argues that such mechanisms ought to be formalized, and that directed acyclic graphs (DAGs) are an appropriate technology of formalization. The response to Pearl by statisticians working in this area is extensive, but beyond the scope of this article.
We use the term “observability” rather than “selection” owing to substantial variation across scholarly communities in the use of the term “selection bias.”
This variation could, of course, be useful as a placebo test for the mechanism.
Anecdotally, an example of the former effect might be the use by local US governments of court fee and fine increases, rather than tax increases, to meet revenue shortfalls (Goldstein et al. 2018). An example of the latter effect might be the selective assignment of property rights to regime supporters (Onoma 2010).
For simplicity, Simpson (2019) uses historical information to classify counties as highly threatened, somewhat threatened, or secure, depending on whether they were contiguous to pivotal castles, contiguous to land held by John, or not.
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We thank Pat Egan, Catherine Hafer, Carlo Horz, Kris Kanthak, Dimitri Landa, Cyrus Samii, and participants at the USC Causal Inference and American Political Development Conference for valuable comments and clarifying discussions.
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Gordon, S.C., Simpson, H.K. Causes, theories, and the past in political science. Public Choice 185, 315–333 (2020). https://doi.org/10.1007/s11127-019-00703-6
- Causal inference
- Quantitative historical analysis