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.
This is a preview of subscription content, access via your institution.
Buy single article
Instant access to the full article PDF.
Tax calculation will be finalised during checkout.
Subscribe to journal
Immediate online access to all issues from 2019. Subscription will auto renew annually.
Tax calculation will be finalised during checkout.
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.
Achen, C. H., & Bartels, L. M. (2016). Democracy for realists: Why elections do not produce responsive government. Princeton, NJ: Princeton University Press.
Badger, A. J. (2007). Whatever happened to Roosevelt’s new generation of southerners? In New Deal/New South (pp. 58–71). University of Arkansas Press, Fayetteville.
Berinsky, A. J. (2006). American public opinion in the 1930s and 1940s: The analysis of quota-controlled sample survey data. Public Opinion Quarterly, 70(4), 499–529.
Berinsky, A . J., Powell, E . N., Schickler, E., & Yohai, I . B. (2011). Revisiting public opinion in the 1930s and 1940s. PS: Political Science & Politics, 44(3), 515–520.
Black, E., & Black, M. (2002). The rise of southern republicans. Cambridge, MA: Belknap Press.
Brady, H. E. (2009). Causation and explanation in social science. In J. M. Box-Steffensmeier, H. E. Brady, & D. Collier (Eds.), The Oxford handbook of political methodology. New York: Oxford University Press.
Brady, H. E., Collier, D., & Seawright, J. (2006). Toward a pluralistic vision of methodology. Political Analysis, 14(3), 353–368.
Burnham, W. D. (1967). Party systems and the political process. In W. N. Chambers & W. D. Burnham (Eds.), The American party systems: Stages of political development (pp. 277–307). New York: Oxford University Press.
Campbell, A. L. (2012). Policy makes mass politics. Annual Review of Political Science, 15, 333–351.
Campbell, D. T., & Stanley, J. (1963). Experimental and quasi-experimental designs for research. Belmont, CA: Wadsworth.
Caughey, D. (2018). The unsolid south: Mass politics and national representation in a one-party enclave. Princeton, NJ: Princeton University Press.
Caughey, D., Berinsky, A. J., Chatfield, S., Hartman, E., Schickler, E., & Sekhon, J. J. (2017). Population estimation and calibration weighting for nonresponse and sampling bias: An application to quota-sampled opinion polls (pp. 1936–1952). Unpublished manuscript.
Caughey, D., & Chatfield, S. (2016). Creating a constituency for New Deal liberalism: The policy feedback effects of the Tennessee Valley Authority. Paper presented at the APSA annual meeting, Philadelphia, PA.
Caughey, D., Dougal, M. C., & Schickler, E. (Forthcoming). Policy and performance in the New Deal realignment: Evidence from old data and new methods. Journal of Politics.
Caughey, D., & Wang, M. (2019). Dynamic ecological inference for time-varying population distributions based on sparse, irregular, and noisy marginal data. Political Analysis, 27, 388–396.
Clapp, C. L. (1963). The congressman: His work as he sees it. Garden City, NY: Anchor Books.
Converse, J. M. (1987). Survey research in the United States: Roots and emergence. Berkeley: University of California Press.
Deville, J.-C., & Särndal, C.-E. (1992). Calibration estimators in survey sampling. Journal of the American Statistical Association, 87(418), 376–382.
Dunning, T. (2012). Natural experiments in the social sciences: A design-based approach. New York: Cambridge University Press.
Hacker, J. S. (2002). The divided welfare state: The battle over public and private social benefits in the United States. New York: Cambridge University Press.
Hartman, E., Grieve, R., Ramsahai, R., & Sekhon, J. S. (2015). From sample average treatment effect to population average treatment effect on the treated: Combining experimental with observational studies to estimate population treatment effects. Journal of the Royal Statistical Society: Series A (Statistics in Society), 178(3), 757–778.
Heckman, J. J., Ichimura, H., & Todd, P. E. (1997). Matching as an econometric evaluation estimator: Evidence from evaluating a job training programme. Review of Economic Studies, 64(4), 605–654.
Holland, P. W. (1986). Statistics and causal inference. Journal of the American Statistical Association, 81(296), 945–960.
Imai, K., Keele, L., Tingley, D., & Yamamoto, T. (2011). Unpacking the black box of causality: Learning about causal mechanisms from experimental and observational studies. American Political Science Review, 105(4), 765–789.
Imai, K., & Yamamoto, T. (2010). Causal inference with differential measurement error: Nonparametric identification and sensitivity analysis. American Journal of Political Science, 54(2), 543–560.
Key, V. O, Jr. (1964). Politics, parties and pressure groups. New York: Crowell.
Kitchens, C. (2014). The role of publicly provided electricity in economic development: The experience of the Tennessee Valley Authority, 1929–1955. Journal of Economic History, 74(2), 389–419.
Kline, P., & Moretti, E. (2014). Local economic development, agglomeration economies, and the big push: 100 years of evidence from the Tennessee Valley Authority. Quarterly Journal of Economics, 129(1), 275–331.
Ladd, E. C., & Hadley, C. D. (1975). Transformations of the American party system: Political coalitions from the New Deal to the 1970s. New York: Norton.
March, J. G., & Olsen, J. P. (1984). The new institutionalism: Organizational factors in political life. American Political Science Review, 78(3), 734–749.
Mayhew, D. R. (1966). Party loyalty among congressmen: The difference between democrats and republicans, 1947–1962. Cambridge, MA: Harvard University Press.
Mayhew, D. R. (2002). Electoral realignments: A critique of an American genre. New Haven, CT: Yale University Press.
Mettler, S., & Valelly, R. (2016). Introduction: The distinctiveness and necessity of American Political Development. In R. Valelly, S. Mettler, & R. Lieberman (Eds.), The Oxford handbook of American political development. Oxford University Press.
Morgan, S . L., & Winship, C. (2015). Counterfactuals and causal inference: Methods and principles for social research (2nd ed.). New York: Cambridge University Press.
Neyman, J. (1923). On the application of probability theory to agricultural experiments. Essay on principles. Section 9. Roczniki Nauk Roiniczych, Tom X (pp. 1–51).
Norpoth, H., Sidman, A. H., & Suong, C. H. (2013). Polls and elections: The New Deal realignment in real time. Presidential Studies Quarterly, 43(1), 146–166.
Orren, K., & Skowronek, S. (2004). The search for American political development. New York: Cambridge University Press.
Paul, L. A., & Hall, N. (2013). Causation: A user’s guide. New York: Oxford University Press.
Pearl, J. (2000). Causality: Models, reasoning, and inference (1st ed.). New York: Cambridge University Press.
Pearl, J. (2009). Causal inference in statistics: An overview. Statistics Surveys, 3, 96–146.
Pierson, P. (1993). When effect becomes cause: Policy feedback and political change. World Politics, 45(4), 595–628.
Pierson, P. (2000). Increasing returns, path dependence, and the study of politics. American Political Science Review, 94(2), 251–267.
Rogers, W. W., Ward, R. D., Atkins, L. R., & Flynt, W. (1994). Alabama: The history of a deep south state. Tuscaloosa: University of Alabama Press.
Rosenbaum, P . R. (2002). Observational studies (2nd ed.). New York: Springer.
Rubin, D. B. (1980). Discussion of “Randomization analysis of experimental data: The Fisher randomization test,” by D. Basu. Journal of the American Statistical Association, 75(371), 591–593.
Rubin, D. B. (2010). Reflections stimulated by the comments of Shadish (2010) and West and Thoemmes (2010). Psychological Methods, 15(1), 38–46.
Särndal, C.-E., & Lundstrom, S. (2005). Estimation in surveys with nonresponse. Hoboken, NJ: Wiley.
Schickler, E. (2001). Disjointed pluralism: Institutional innovation and the development of the U.S. Congress. Princeton, NJ: Princeton University Press.
Schulman, B. J. (1994). From Cotton Belt to Sunbelt: Federal policy, economic development, and the transformation of the South, 1938–1980. Durham, NC: Duke University Press.
Sekhon, J. S. (2008). The Neyman–Rubin model of causal inference and estimation via matching methods. In J. M. Box-Steffensmeier, H. E. Brady, & D. Collier (Eds.), The Oxford handbook of political methodology (pp. 271–299). New York: Oxford University Press.
Sekhon, J. S. (2011). Multivariate and propensity score matching software with automated balance optimization: The Matching package for R. Journal of Statistical Software, 42(7), 1–52.
Sen, M., & Wasow, O. (2016). Race as a bundle of sticks: Designs that estimate effects of seemingly immutable characteristics. Annual Review of Political Science, 19(1), 499–522.
Shadish, W. R. (2010). Campbell and Rubin: A primer and comparison of their approaches to causal inference in field settings. Psychological Methods, 15(1), 3–17.
Sundquist, J. L. (1983). Dynamics of the party system: Alignment and realignment of political parties in the United States (Revised ed.). Washington, DC: Brookings.
Thelen, K. (1999). Historical institutionalism in comparative politics. Annual Review of Political Science, 2, 369–404.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
About this article
Cite this article
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
- Causal inference
- American political development
- Survey research
- Policy feedback