Abstract
This article compares statistical and set-theoretic approaches to causal analysis. Statistical researchers commonly use additive, linear causal models, whereas set-theoretic researchers typically use logic-based causal models. These models differ in many fundamental ways, including whether they assume symmetric or asymmetrical causal patterns, and whether they call attention to equifinality and combinatorial causation. The two approaches also differ in how they utilize counterfactuals and carry out counterfactual analysis. Statistical researchers use counterfactuals to illustrate their results, but they do not use counterfactual analysis for the goal of causal model estimation. By contrast, set-theoretic researchers use counterfactuals to estimate models by making explicit their assumptions about empty sectors in the vector space defined by the causal variables. The paper concludes by urging greater appreciation of the differences between the statistical and set-theoretic approaches to causal analysis.
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Notes
- 1.
There are, of course, statistical methods for estimating nonlinear models, for example, Bates and Watts (1988), but these often are not taught or used in political science or sociology.
- 2.
This is a key point in the philosophical–statistical literature on causation as well (e.g., Cartwright 1989, 55–56).
- 3.
We think that much of the discussion of equifinality inappropriately views its distinctive aspect as the representation of combinations of factors. If one focuses mainly on this aspect using a statistical perspective, as do King et al. (1994, 87–89), one may believe (inappropriately) that equifinality is simply a way of talking about interaction terms.
- 4.
In the social sciences, researchers rarely propose a single factor that is sufficient all by itself for a positive outcome. Instead, multiple causes that are jointly sufficient for the outcome are grouped together.
- 5.
Of course, it is likely that variation in above average GRE statistical scores contributed to the outcome as well. In our example, high quantitative GREs alone are not close to being sufficient for admission.
- 6.
Obviously, the data in Table 5.2 are simplistic in various ways. With real data, for example, one might well have “contradictory cases” in which the same configuration of values on the causal variables is associated with different outcomes. We keep things simple here for illustrative purposes.
- 7.
Researchers who use set-theoretic ideas informally in small-N and case-study research make these same assessments, though in a more implicit and ad hoc fashion.
- 8.
If one conducted an OLS regression (not advised for dichotomous variables), one gets an R 2 of 1.0 because the strong left party variable is a prefect predictor. By contrast, the strong unions variable is not significant (Schneider and Wagemann 2012).
References
Bates, D. M., & Watts, D. G. (1988). Nonlinear regression analysis and its applications. New York: Wiley.
Braumoeller, B. (2003). Causal complexity and the study of politics. Political Analysis, 11, 209–233.
Cartwright, N. (1989). Nature’s capacities and their measurement. Oxford: Oxford University Press.
Collins, J., Hall, N., & Paul, L. A., (Eds.) (2004). Causation and counterfactuals. Cambridge: MIT Press.
Esping-Andersen, G. (1990). The three worlds of welfare capitalism. Cambridge: Polity Press.
George, A., & Bennett, A. (2005). Case studies and theory development in the social sciences. Cambridge: MIT Press.
Goertz, G., & Starr, H., (Eds.) (2003). Necessary conditions: theory, methodology, and applications. New York: Rowman and Littlefield.
Hicks, A., Misra, J., & Ng, T. N. (1995). The programmatic emergence of the social security state. American Sociological Review, 60, 329–350.
Jackman, S. (2009). Bayesian analysis for the social sciences. New York: Wiley.
King, G., Keohane, R. O., & Verba, S. (1994). Designing social inquiry: Scientific inference in qualitative research. Princeton: Princeton University Press.
Lewis, D. (1973). Counterfactuals. Cambridge: Harvard University Press.
Lieberman, E. S. (2005). Nested analysis as a mixed-method strategy for comparative research. American Political Science Review, 99, 435–452.
Mahoney, J. (2010). Colonialism and postcolonial development: Spanish America in comparative perspective. Cambridge: Cambridge University Press.
Moore, Jr., B. (1966). Social origins of dictatorship and democracy: lord and peasant in the making of the modern world. Boston: Beacon Press.
Morgan, S. L., & Winship, C. (2007). Counterfactuals and causal inference: Methods and principles for social research. Cambridge: Cambridge University Press.
Ragin, C. C. (1987). The comparative method: Moving beyond qualitative and statistical strategies. Berkeley: University of California Press.
Ragin, C. C. (2000). Fuzzy-set social science. Chicago: University of Chicago Press.
Ragin, C. C. (2008). Redesigning social inquiry: Fuzzy sets and beyond. Chicago: University of Chicago Press.
Rubin, D. B. (1974). Estimating causal effects of treatments in randomized and nonrandomized studies. Journal of Education Psychology, 66, 688–701.
Rubin, D. B. (1990). Comment: Neyman and causal inference in experiments and observational studies. Statistical Science, 5, 472–480.
Schneider, C., & Wagemann, C. (2012). Set-theoretic methods for the social sciences: A guide to qualitative comparative analysis. New York: Cambridge University Press.
Schrodt, P. A. (2010). Seven deadly sins of contemporary statistical political analysis. Paper presented at the annual meetings of the American Political Science Association, Washington, DC.
Stokke, O. S. (2007). Qualitative comparative analysis, shaming, and international regime effectiveness. Journal of Business Research, 60, 501–511.
Vaisey, S. (2009). QCA 3.0: The Ragin Revolution Continues. Contemporary Sociology, 38, 308–312.
Wickham-Crowley, T. (1992). Guerrillas and revolution in Latin America: A comparative study of insurgents and regimes since 1956. Princeton: Princeton University Press.
Acknowledgements
We thank Stephen Morgan and Judea Pearl for helpful comments on a previous version.
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Mahoney, J., Goertz, G., Ragin, C.C. (2013). Causal Models and Counterfactuals. In: Morgan, S. (eds) Handbook of Causal Analysis for Social Research. Handbooks of Sociology and Social Research. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-6094-3_5
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