Abstract
Causal inference and American political development (APD) are widely separated and (to some) fundamentally incompatible tendencies within political science. In this paper, we explore points of connection between those two perspectives, while also highlighting differences that are not so easily bridged. We stress that both causal inference and APD are centrally interested in questions of causation, but they approach causation with very different ontological and epistemological commitments. We emphasize how the sort of detailed, contextualized, and often qualitative knowledge privileged by APD can promote credible causal (and descriptive) inferences, but also that scholars of causal inference can benefit from alternate conceptions of causality embraced by APD work. We illustrate with two empirical examples from our own research: devising weights for quota-sampled opinion polls and estimating the political effects of the Tennessee Valley Authority. We conclude that bringing APD and causal inference together on more equal terms may require a broader perspective on causation than is typical of scholarship in the causal-inference tradition.
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Notes
Rubin has sought to address the problem of generalization in the absence of random sampling by using Bayesian predictive models for the potential outcomes, which he considers the “third leg of the RCM” (Rubin 2010, p. 45). To our knowledge, however, that approach rarely if ever has been implemented by political scientists.
A third influential perspective on causal inference is that associated with the social psychologist Donald Campbell (Campbell and Stanley 1963; Shadish 2010). Like Rubin, Campbell is centrally concerned with research design as a basis for causal inference. More than Rubin, however, Campbell focuses on specific threats to causal inference (“internal validity”) and on generalizing causal effects (“external validity”).
Berinsky et al. (2011) and Caughey et al. (2017) have been funded by two National Science Foundation grants: SES-0550431 (Adam Berinsky and Eric Schickler, “Collaborative Research: The American Mass Public in the 1930s and 1940s,” 2006–2010) and SES-1155143 (Adam Berinsky, Eric Schickler, and Jasjeet S. Sekhon, “Collaborative Research: The American Mass Public in the Early Cold War Years,” 2012–2014).
The severe undersampling of Southern (but not non-Southern) African Americans sometimes requires the even more drastic response of redefining the target population to what we call the “voting-eligible population” (i.e., white Southerners plus all non-Southerners).
Even Mayhew (2002), who generally is critical of realignment theory, finds that the New Deal realignment holds up better than other putative cases of partisan realignment.
Some scholars who date the New Deal realignment to the 1930s attribute it solely to retrospective evaluations of the economy, as opposed to policy evaluations of the New Deal itself (Achen and Bartels 2016; but see Caughey et al. Forthcoming).
Note that the gap between the weighted and unweighted estimates narrows over time, which coincides with the gradual improvement in polling organizations’s sampling techniques and their gradual transition to probability sampling after 1948.
It also should be noted that Norpoth et al. (2013) present a good deal of micro-level evidence consistent with their theory.
The years refer to election years; members’ conservatism is measured in the following congressional term (e.g., 1933–1934 for 1932).
By (good) explanation, we mean a statement of valid premises from which the outcome of interest follows necessarily or with high probability, and that specifies the mechanism by which the premise entails the outcome.
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Caughey, D., Chatfield, S. Causal inference and American political development: contrasts and complementarities. Public Choice 185, 359–376 (2020). https://doi.org/10.1007/s11127-019-00694-4
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DOI: https://doi.org/10.1007/s11127-019-00694-4