Types of Causes

  • Jeremy FreeseEmail author
  • J. Alex Kevern
Part of the Handbooks of Sociology and Social Research book series (HSSR)


The complexity of actual cause and effect relationships in social life can lead quickly to confused thinking and muddled discussions. Helpful here are distinctions that allow one to speak about some causes as different from others. Our chapter describes several distinctions among causes that we find especially useful for social science. First, taking a broad view of what “causes” are, we discuss some issues concerning whether causes are manipulable or preventable. Then, we consider the distinction between proximal and distal causes, connecting these to concepts of mediation and indirect effects. Next, we propose ways that concepts related to the distinction between necessary and sufficient causes in case-oriented research may be also useful for quantitative research on large samples. Afterward, we discuss criteria for characterizing one cause as more important than another. Finally, we describe ultimate and fundamental causes, which do not concern the relationship between an explanatory variable and outcome so much as the causes of properties of the systems in which more concrete causal relationships exist.


Granger Causality Standardize Coefficient School Choice Attributable Fraction Educational Difference 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. Belsky, J., Steinberg, L., & Draper, P. (1991). Childhood experience, interpersonal development, and reproductive strategy: An evolutionary theory of socialization. Child Development, 62, 647–670.CrossRefGoogle Scholar
  2. Bennett, A. (2010). Process tracing and causal inference. In H. E. Brady & D. Collier (Eds.), Rethinking social inquiry: Diverse tools, shared standards (2nd ed.). Lanham: Rowman & Littlefield.Google Scholar
  3. Blalock, H. M. (1961). Evaluating the relative importance of variables. American Sociological Review, 26, 866–874.CrossRefGoogle Scholar
  4. Brady, H. E., & Collier, A. (2010). Rethinking social inquiry: Diverse tools, shared standards (2nd ed.). Lanham: Rowman & Littlefield.Google Scholar
  5. Branigan, A. R., Freese, J., Patir, A., McDade, T. W., Liu, K., & Kiefe, C. (2011). Skin color, sex, and educational attainment in the post-civil-rights era. Presented at the Meeting of the Research Committee on Social Stratification and Mobility (RC28) of the International Sociological Association. Essex, UK.Google Scholar
  6. Collins, J., Hall, N., & Paul, L. A. (Eds.). (2004). Causation and counterfactuals. Cambridge, MA: MIT Press.Google Scholar
  7. Danaei, G., Ding, E. L., Dariush, M., Ben, T., Jurgen, R., Murray, C. J. L., & Majid, E. (2009). The preventable causes of death in the United States: Comparative risk assessment of dietary, lifestyle, and metabolic risk factors. PLoS Medicine, 6, e1000058.CrossRefGoogle Scholar
  8. Dion, D. (1998). Evidence and inference in the comparative case study. Comparative Politics, 30, 127–145.CrossRefGoogle Scholar
  9. Flanders, D. (2006). On the relationship of sufficient component cause models with potential outcome (counterfactual) models. European Journal of Epidemiology, 21, 847–853.CrossRefGoogle Scholar
  10. Freese, J. (2008). Genetics and the social science explanation of individual outcomes. American Journal of Sociology, 114, S1–S35.CrossRefGoogle Scholar
  11. Freese, J., & Lutfey, K. E. (2011). Fundamental causality: Challenges of an animating concept for medical sociology. In B. Pescosolido, J. Martin, J. McLeod, & A. Rogers (Eds.), Handbook of medical sociology. New York: Springer.Google Scholar
  12. Granger, C. W. J. (1969). Investigating causal relations by econometric models and cross-spectral models. Econometrica, 37, 424–438.CrossRefGoogle Scholar
  13. Greenland, S., & Robins, J. (1988). Conceptual problems in the definition and interpretation of attributable fractions. American Journal of Epidemiology, 128, 1185–1197.Google Scholar
  14. Greenland, S., & Rothman, K. J. (2008). Introduction to stratified analysis. In K. J. Rothman, S. Greenland, & T. L. Lash (Eds.), Modern epidemiology (3rd ed., pp. 258–283). Philadelphia: Lippincott, Williams, & Wilkins.Google Scholar
  15. Hall, N. (2004). Two concepts of causation. In J. Collins, N. Hall, & L. A. Paul (Eds.), Causation and counterfactuals. Cambridge, MA: MIT Press.Google Scholar
  16. Hargens, L. L. (1976). A note on standardized coefficients as structural parameters. Sociological Methods and Research, 5, 247–256.CrossRefGoogle Scholar
  17. Holland, P. (1986). Statistics and causal inference. Journal of the American Statistical Association, 81, 945–960.CrossRefGoogle Scholar
  18. Holland, P. W. (2003). Causation and race (Educational Testing Service Research Report RR-03-03).Google Scholar
  19. Johansson, I., & Lynøe, N. (2008). Medicine and philosophy: A twenty-first century introduction. Piscataway: Transaction Books.Google Scholar
  20. King, G. (1986). How not to lie with statistics: Avoiding common mistakes in quantitative political science. American Journal of Political Science, 30, 666–687.CrossRefGoogle Scholar
  21. Laland, K. N., Sterelny, K., Odling-Smee, J., Hoppitt, W., & Uller, T. (2011). Cause and effect in biology revisited: Is Mayr’s proximate-ultimate distinction still useful? Science, 334, 1512–1516.CrossRefGoogle Scholar
  22. Leahey, E. (2007). Not by productivity alone: How visibility and specialization contribute to academic earnings. American Sociological Review, 72, 533–561.CrossRefGoogle Scholar
  23. Lieberson, S. (1985). Making it count: The improvement of social research and theory. Berkeley/Los Angeles: University of California Press.Google Scholar
  24. Lieberson, S. (1991). Small N’s and big conclusions: An examination of the reasoning in comparative studies based on a small number of cases. Social Forces, 70, 307–320.Google Scholar
  25. Link, B. G., & Phelan, J. C. (1995). Social conditions as fundamental causes of disease. Journal of Health and Social Behavior, 35, 80–94.CrossRefGoogle Scholar
  26. Lutfey, K., & Freese, J. (2005). Toward some fundamentals of fundamental causality: Socioeconomic status and health in the routine clinic visit for diabetes. The American Journal of Sociology, 110, 1326–1372.CrossRefGoogle Scholar
  27. Mackie, J. L. (1965). Causes and conditions. American Philosophical Quarterly, 2, 245–264.Google Scholar
  28. Mahoney, J. (2008). Toward a unified theory of causality. Comparative Political Studies, 41, 412–436.CrossRefGoogle Scholar
  29. Mahoney, J., Kimball, E., & Koivu, K. L. (2009). The logic of historical explanation in the social sciences. Comparative Political Studies, 42, 114–146.CrossRefGoogle Scholar
  30. Martin, J. L. (2011). The explanation of social action. Oxford: Oxford University Press.CrossRefGoogle Scholar
  31. Mayr, E. (1961). Cause and effect in biology. Science, 134, 1501–1506.CrossRefGoogle Scholar
  32. Morgan, S. L., & Winship, C. (2007). Counterfactuals and causal inference: Methods and principles for social research. Cambridge, UK: Cambridge University Press.Google Scholar
  33. Pearl, J. (2009). Causality: Models, reasoning, and inference (2nd ed.). Cambridge: Cambridge University Press.CrossRefGoogle Scholar
  34. Phelan, J. C., Link, B. G., & Tehranifar, P. (2010). Social conditions as fundamental causes of health inequalities: Theory, evidence, and policy implications. Journal of Health and Social Behavior, 51(1), S28–S40.CrossRefGoogle Scholar
  35. Ragin, C. C. (2000). Fuzzy-set social science. Chicago: University of Chicago Press.Google Scholar
  36. Rothman, K. J., & Greenland, S. (2005). Causation and causal inference in epidemiology. American Journal of Public Health, 95, S144–S150.CrossRefGoogle Scholar
  37. Rutter, M. (2006). Genes and behavior: Nature-nurture interplay explained. Malden: Blackwell.Google Scholar
  38. Stinchcombe, A. (1968). Constructing social theories. New York: Harcourt, Brace & World.Google Scholar
  39. Treiman, D. J. (2009). Quantitative data analysis: Doing social research to test ideas. San Francisco: Jossey-Bass.Google Scholar
  40. VanderWeele, T., & Robins, J. M. (2007). The identification of synergism in the sufficient-component-cause framework. Epidemiology, 18, 329–339.CrossRefGoogle Scholar
  41. Winship, C., & Sobel, M. (2004). Causal inference in sociological studies. In M. Hardy & A. Bryman (Eds.), Handbook of data analysis. Thousand Oaks: Sage.Google Scholar
  42. Wright, E. O., Levine, A., & Sober, E. (1992). Reconstructing Marxism: Essays on explanation and the theory of history. London: Verso.Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  1. 1.Department of Sociology and Institute for Policy ResearchNorthwestern UniversityEvanstonUSA
  2. 2.Department of SociologyNorthwestern UniversityEvanstonUSA

Personalised recommendations