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Why Process Matters for Causal Inference

Published online by Cambridge University Press:  04 January 2017

Adam N. Glynn  and
Kevin M. Quinn
Adam N. Glynn*
Affiliation:
Department of Government, Harvard University, 1737 Cambridge Street, Cambridge, MA 02138
Kevin M. Quinn
Affiliation:
UC Berkeley School of Law, 490 Simon 7200, Berkeley, CA 94720-7200. e-mail: [email protected]
*
e-mail: [email protected] (corresponding author)
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Abstract

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Our goal in this paper is to provide a formal explanation for how within-unit causal process information (i.e., data on posttreatment variables and partial information on posttreatment counterfactuals) can help to inform causal inferences relating to total effects—the overall effect of an explanatory variable on an outcome variable. The basic idea is that, in many applications, researchers may be able to make more plausible causal assumptions conditional on the value of a posttreatment variable than they would be able to do unconditionally. As data become available on a posttreatment variable, these conditional causal assumptions become active and information about the effect of interest is gained. This approach is most beneficial in situations where it is implausible to assume that treatment assignment is conditionally ignorable. We illustrate the approach with an example of estimating the effect of election day registration on turnout.

Type
Research Article
Information
Political Analysis , Volume 19 , Issue 3 , Summer 2011 , pp. 273 - 286
Copyright
Copyright © The Author 2011. Published by Oxford University Press on behalf of the Society for Political Methodology 

Footnotes

Authors' note: The authors thank Matt Chingos for his research assistance and Kevin Clarke, Luke Keele, Jay Kaufman, James Mahoney, the participants of the 2009 meeting of the Midwest Political Science Association, the participants of the 2009 Causal Workshop at the Banff International Research Station, the participants of the 2009 Summer meeting of the Society of Political Methodology, two anonymous referees, and the editors for their helpful comments and suggestions.

References

Achen, C. H. 2008. Registration and voting under rational expectations: the econometric implications. Paper presented at the summer meeting of the society for political methodology, Ann Arbor, MI.Google Scholar
Angrist, Joshua D., Imbens, Guido W., and Rubin, Donald B. 1996. Identification of causal effects using instrumental variables. Journal of the American Statistical Association 91: 444–55.Google Scholar
Balke, Alexander. 1995. Probabilistic counterfactuals: semantics, computation, and applications. PhD diss., University of California, Los Angeles.Google Scholar
Brady, Henry E., Collier, David, and Seawright, Jason. 2006. Toward a pluralistic vision of methodology. Political Analysis 14: 353–68.Google Scholar
Cochran, William G. 1965. The planning of observational studies of human populations (with Discussion). Journal of the Royal Statistical Society Series A 128: 134–55.CrossRefGoogle Scholar
Collier, David, and Brady, Henry E. 2004. Rethinking social inquiry: Diverse tools, shared standards. Lanham, MD: Rowman & Littlefield.Google Scholar
Copas, J. B. 1973. Randomization models for the matched and unmatched 2 × 2 tables. Biometrika 60: 467.Google Scholar
Deaton, Angus. 2009. Instruments of development: Randomization in the tropics, and the search for the elusive keys to economic development. NBER Working paper no. 14690.Google Scholar
Frangakis, C. E., and Rubin, D. B. 2002. Principal stratification in causal inference. Biometrics 58: 21–9.Google Scholar
George, A. L., and Bennett, A. 2005. Case studies and theory development in the social sciences. Cambridge, MA: MIT Press.Google Scholar
Glynn, A. N. Forthcoming 2011. The product and difference fallacies for indirect effects. American Journal of Political Science.Google Scholar
Green, D. P. 2004. Mobilizing African-American voters using direct mail and commercial phone banks: A field experiment. Political Research Quarterly 57: 245.CrossRefGoogle Scholar
Hanmer, M. J. 2007. An alternative approach to estimating who is most likely to respond to changes in registration laws. Political Behavior 29(1): 1–30.Google Scholar
Heckman, James, and Urzua, Sergio. 2009. Comparing IV with structural models: What simple IV can and cannot identify. NBER Working paper no 14706.CrossRefGoogle Scholar
Hedström, Peter. 2008. Studying mechanisms to strengthen causal inferences in quantitative research. In The Oxford Handbook of Political Methodology, eds. Box-Steffensmeier, Janet, Brady, Henry E., and Collier, David. Oxford: Oxford University Press.Google Scholar
Highton, B. 1997. Easy registration and voter turnout. Journal of Politics 59: 565–75.Google Scholar
Highton, B. 2004. Voter registration and turnout in the United States. Perspectives on Politics 2: 507–15.Google Scholar
Ho, Daniel E., Imai, Kosuke, King, Gary, and Stuart, Elizabeth A. 2007. Matching as nonparametric preprocessing for reducing model dependence in parametric causal inference. Political Analysis 15: 199–236.Google Scholar
Holland, Paul W. 1986. Statistics and causal inference. Journal of the American Statistical Association 81: 945–60.Google Scholar
Imai, Kosuke, Keele, Luke, and Yamamoto, Teppei. 2010. Identification, inference, and sensitivity analysis for causal mediation effects. Statistical Science 25: 51–71.Google Scholar
Joffe, M. M. 2001. Using information on realized effects to determine prospective causal effects. Journal of the Royal Statistical Society: Series B, Statistical Methodology 759–74.Google Scholar
Kaufman, Sol, Kaufman, Jay S., and MacLehose, Richard F. 2009. Analytic bounds on causal risk differences in directed acyclic graphs with three observed binary variables. Journal of Statistical Planning and Inference 139: 3473–87.Google Scholar
King, Gary. 1991. “Truth” is stranger than prediction, more questionable than causal inference. American Journal of Political Science 35: 1047–53.Google Scholar
King, G., Keohane, R. O., and Verba, S. 1994. Designing social inquiry: Scientific inference in qualitative research. Princeton, NJ: Princeton Univ Press.Google Scholar
Kuroki, M., and Cai, Z. 2008. The evaluation of causal effects in studies with an unobserved exposure/outcome variable: Bounds and identification. Proceedings from the 2008 Conference of Uncertainty in Artificial Intelligence, Helsinki, Finland.Google Scholar
Manski, Charles F. 2003. Partial identification of probability distributions. New York: Springer.Google Scholar
Nagler, J. 1991. The effect of registration laws and education on US voter turnout. The American Political Science Review 85: 1393–405.Google Scholar
Neyman, J. 1923. On the application of probability theory to agricultural experiments: Essay on principles, Section 9. (translated in 1990). Statistical Science 5: 465–80.Google Scholar
Pearl, Judea. 1995. Causal diagrams for empirical research. Biometrika 82: 669–710.Google Scholar
Pearl, Judea. 2001. Direct and indirect effects. Proceedings of the Seventeenth Conference on Uncertainty in Artificial Intelligence, Seattle, WA. pp. 411-20.Google Scholar
Pearl, Judea. 2009. Causality: Models, reasoning, and inference. 2nd ed. New York: Cambridge University Press.Google Scholar
Pearl, Judea. 2010. The Mediation Formula: A guide to the assessment of causal pathways in non-linear models. Technical report R-363.Google Scholar
Powell, G. B. Jr 1986. American voter turnout in comparative perspective. The American Political Science Review 80: 17–43.Google Scholar
Robins, J. M. 2003. Semantics of causal DAG models and the identification of direct and indirect effects. In Highly Structured Stochastic Systems, eds. Green, Peter J., Hjort, Nils Lid, and Richardson, Sylvia, 70–81. Oxford: Oxford University Press.Google Scholar
Robins, J. M., and Greenland, S. 1992. Identifiability and exchangeability for direct and indirect effects. Epidemiology 3: 143–55.Google Scholar
Rosenbaum, Paul R. 1984. The consequences of adjustment for a concomitant variable that has been affected by the treatment. Journal of the Royal Statistical Society: Series A 147: 656–66.Google Scholar
Rosenbaum, Paul R., and Rubin, Donald B. 1983. The Central role of the propensity score in observational studies for causal effects. Biometrika 70: 41–55.Google Scholar
Rosenstone, S. J., and Wolfinger, R. E. 1978. The effect of registration laws on voter turnout. Science Review 72: 22–45.Google Scholar
Rubin, Donald B. 1978. Bayesian inference for causal effects: The role of randomization. The Annals of Statistics 6(1): 34–58.CrossRefGoogle Scholar
Sekhon, Jasjeet S. 2008. The Neyman-Rubin model of causal inference and estimation via matching methods. The Oxford Handbook of Political Methodology, ed. Box-Steffensmeier, Janet M., Brady, Henry E., and Collier, David. New York: Oxford University Press.Google Scholar
Sekhon, Jasjeet Singh. 2011. Multivariate and propensity score matching software with automated balance optimization: the matching package for R. Journal of Statistical Software 42: 1–52.Google Scholar
TenHave, Thomas R., Elliot, Michael R., Joffe, Marshall, Zanutto, Elaine, and Datto, Catherine. 2004. Causal models for randomized physician encouragement trials in testing primary care depression. Journal of the American Statistical Association 99(465): 16–25.Google Scholar
Tian, J., and Pearl, J. 2002a. A general identification condition for causal effects. Proceedings of the National Conference on Artificial Intelligence. Menlo Park, CA: AAAI Press/MIT Press. pp. 567-73.Google Scholar
Tian, J., and Pearl, J. 2002b. On the identification of causal effects. Proceedings of the American Association of Artificial Intelligence. Menlo Park, CA: AAAI Press/MIT Press.Google Scholar
Timpone, R. J. 1998. Structure, behavior, and voter turnout in the United States. American Political Science Review 92: 145–58.Google Scholar
VanderWeele, T. J. 2008. The sign of the bias of unmeasured confounding. Biometrics 64: 702–06.Google Scholar
VanderWeele, T. J., and Robins, J. M. 2010. Signed directed acyclic graphs for causal inference. Journal Royal Statistical Society Series B 72: 111–27.CrossRefGoogle ScholarPubMed