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Causal inference and American political development: contrasts and complementarities

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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

  1. 1.

    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.

  2. 2.

    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”).

  3. 3.

    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).

  4. 4.

    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).

  5. 5.

    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.

  6. 6.

    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).

  7. 7.

    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.

  8. 8.

    It also should be noted that Norpoth et al. (2013) present a good deal of micro-level evidence consistent with their theory.

  9. 9.

    The years refer to election years; members’ conservatism is measured in the following congressional term (e.g., 1933–1934 for 1932).

  10. 10.

    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.

References

  1. Achen, C. H., & Bartels, L. M. (2016). Democracy for realists: Why elections do not produce responsive government. Princeton, NJ: Princeton University Press.

    Google Scholar 

  2. 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.

  3. 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.

    Google Scholar 

  4. 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.

    Google Scholar 

  5. Black, E., & Black, M. (2002). The rise of southern republicans. Cambridge, MA: Belknap Press.

    Google Scholar 

  6. 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.

    Google Scholar 

  7. Brady, H. E., Collier, D., & Seawright, J. (2006). Toward a pluralistic vision of methodology. Political Analysis, 14(3), 353–368.

    Google Scholar 

  8. 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.

    Google Scholar 

  9. Campbell, A. L. (2012). Policy makes mass politics. Annual Review of Political Science, 15, 333–351.

    Google Scholar 

  10. Campbell, D. T., & Stanley, J. (1963). Experimental and quasi-experimental designs for research. Belmont, CA: Wadsworth.

    Google Scholar 

  11. Caughey, D. (2018). The unsolid south: Mass politics and national representation in a one-party enclave. Princeton, NJ: Princeton University Press.

    Google Scholar 

  12. 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.

  13. 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.

  14. 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.

  15. 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.

    Google Scholar 

  16. Clapp, C. L. (1963). The congressman: His work as he sees it. Garden City, NY: Anchor Books.

    Google Scholar 

  17. Converse, J. M. (1987). Survey research in the United States: Roots and emergence. Berkeley: University of California Press.

    Google Scholar 

  18. Deville, J.-C., & Särndal, C.-E. (1992). Calibration estimators in survey sampling. Journal of the American Statistical Association, 87(418), 376–382.

    Google Scholar 

  19. Dunning, T. (2012). Natural experiments in the social sciences: A design-based approach. New York: Cambridge University Press.

    Google Scholar 

  20. 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.

    Google Scholar 

  21. 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.

    Google Scholar 

  22. 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.

    Google Scholar 

  23. Holland, P. W. (1986). Statistics and causal inference. Journal of the American Statistical Association, 81(296), 945–960.

    Google Scholar 

  24. 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.

    Google Scholar 

  25. 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.

    Google Scholar 

  26. Key, V. O, Jr. (1964). Politics, parties and pressure groups. New York: Crowell.

    Google Scholar 

  27. 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.

    Google Scholar 

  28. 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.

    Google Scholar 

  29. 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.

    Google Scholar 

  30. March, J. G., & Olsen, J. P. (1984). The new institutionalism: Organizational factors in political life. American Political Science Review, 78(3), 734–749.

    Google Scholar 

  31. Mayhew, D. R. (1966). Party loyalty among congressmen: The difference between democrats and republicans, 1947–1962. Cambridge, MA: Harvard University Press.

    Google Scholar 

  32. Mayhew, D. R. (2002). Electoral realignments: A critique of an American genre. New Haven, CT: Yale University Press.

    Google Scholar 

  33. 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.

  34. Morgan, S . L., & Winship, C. (2015). Counterfactuals and causal inference: Methods and principles for social research (2nd ed.). New York: Cambridge University Press.

    Google Scholar 

  35. 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).

  36. 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.

    Google Scholar 

  37. Orren, K., & Skowronek, S. (2004). The search for American political development. New York: Cambridge University Press.

    Google Scholar 

  38. Paul, L. A., & Hall, N. (2013). Causation: A user’s guide. New York: Oxford University Press.

    Google Scholar 

  39. Pearl, J. (2000). Causality: Models, reasoning, and inference (1st ed.). New York: Cambridge University Press.

    Google Scholar 

  40. Pearl, J. (2009). Causal inference in statistics: An overview. Statistics Surveys, 3, 96–146.

    Google Scholar 

  41. Pierson, P. (1993). When effect becomes cause: Policy feedback and political change. World Politics, 45(4), 595–628.

    Google Scholar 

  42. Pierson, P. (2000). Increasing returns, path dependence, and the study of politics. American Political Science Review, 94(2), 251–267.

    Google Scholar 

  43. 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.

    Google Scholar 

  44. Rosenbaum, P . R. (2002). Observational studies (2nd ed.). New York: Springer.

    Google Scholar 

  45. 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.

    Google Scholar 

  46. Rubin, D. B. (2010). Reflections stimulated by the comments of Shadish (2010) and West and Thoemmes (2010). Psychological Methods, 15(1), 38–46.

    Google Scholar 

  47. Särndal, C.-E., & Lundstrom, S. (2005). Estimation in surveys with nonresponse. Hoboken, NJ: Wiley.

    Google Scholar 

  48. Schickler, E. (2001). Disjointed pluralism: Institutional innovation and the development of the U.S. Congress. Princeton, NJ: Princeton University Press.

    Google Scholar 

  49. 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.

    Google Scholar 

  50. 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.

    Google Scholar 

  51. 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.

    Google Scholar 

  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.

    Google Scholar 

  53. 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.

    Google Scholar 

  54. Sundquist, J. L. (1983). Dynamics of the party system: Alignment and realignment of political parties in the United States (Revised ed.). Washington, DC: Brookings.

    Google Scholar 

  55. Thelen, K. (1999). Historical institutionalism in comparative politics. Annual Review of Political Science, 2, 369–404.

    Google Scholar 

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Correspondence to Sara Chatfield.

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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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Keywords

  • Causal inference
  • American political development
  • Survey research
  • Policy feedback

JEL Classification

  • N4
  • C0
  • H4