Skip to main content

Part of the book series: The Springer Series on Demographic Methods and Population Analysis ((PSDE,volume 23))

It is impossible to escape the impression that population scientists commonly use false standards in adducing causation – that they seek to make claims about the power of their research in elucidating cause and effect and admire similar claims in others, and that they mis-estimate the true values of important causal parameters. And yet, in making any general judgment of this sort, we are in danger of forgetting how variegated the human population and the mental constructs associated with its apprehension are.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

eBook
USD 16.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  • Allison, P.D. (2005). Fixed Effects Regression Methods for Longitudinal Data Using SAS ®. Cary, NC: SAS Institute Inc.

    Google Scholar 

  • Angrist, J.D., G.W. Imbens and D.B. Rubin (1996). Identification of Causal Effects Using Instrumental Variables. Journal of the American Statistical Association 91(434): 444–455.

    Article  Google Scholar 

  • Bachrach, C. and G. McNicoll (2003). Introduction [to Causal Analysis in the Population Sciences: A Symposium. Population and Development Review 29(3): 443–447.

    Article  Google Scholar 

  • Berk, R.A., K.J. Lenihan and P.H. Rossi (1980). Crime and Poverty: Some Experimental Evidence from Ex-Offenders. American Sociological Review 45(5): 766–786.

    Article  Google Scholar 

  • Berk, R.A. (2004). Regression Analysis: A Constructive Critique. Thousand Oaks, CA: Sage Publications.

    Google Scholar 

  • Caldwell, J.C. (1996). Demography and Social Science. Population Studies 50(3): 305–333.

    Article  Google Scholar 

  • Caldwell, J.C., P.H. Reddy and P. Caldwell (1988). The Causes of Demographic Change: Experimental Research in South India. Madison, WI: University of Wisconsin Press.

    Google Scholar 

  • Campbell, D.T. and J.C. Stanley (1963). Experimental and Quasi-Experimental Designs for Research. Chicago: Rand-McNally.

    Google Scholar 

  • Coale, A. and J. Trussell (1996). The Development and Use of Demographic Models. Population Studies 50(3): 469–484.

    Article  Google Scholar 

  • Coser, L.A. (1956). The Functions of Social Conflict. New York: The Free Press.

    Google Scholar 

  • Crimmins, E.M. (1993). Demography: The Past Thirty Years, the Present, and the Future. Demography 30(4): 579–591.

    Article  Google Scholar 

  • Duncan, O.D. (1975). Introduction to Structural Equation Modeling. New York: Academic Press.

    Google Scholar 

  • Duncan, O.D., D.L. Feathernan and B. Duncan (1972). Socioeconomic Background and Achievement. New York: Seminar Press.

    Google Scholar 

  • Frangakis, C.E. (2004). Principal Stratification. In: Applied Bayesian Modeling and Causal Inference from Incomplete-Data Perspectives: An Essential Journey with Donald Rubin’s Statistical Family, eds. Andrew Gelman and Xiao-Li Meng. Chichester, England: John Wiley & Sons Ltd.

    Google Scholar 

  • Frangakis, C.E. and Donald B. Rubin (2002). Principal Stratification in Causal Inference. Biometrics 58(1): 21–29.

    Google Scholar 

  • Freedman, D. (1987). As Others See Us: A Case Study in Path Analysis. Journal of Educational and Behavioral Statistics12(2): 101–128.

    Article  Google Scholar 

  • Freud, S. (1961). Civilization and Its Discontents. Translated and edited by James Strachey. New York: W. W. Norton & Company, Inc.

    Google Scholar 

  • Gelman, A. and X.-L. Meng (eds.) (2004). Applied Bayesian Modeling and Causal Inference from Incomplete-Data Perspectives: An Essential Journey with Donald Rubin’s Statistical Family. Chichester, England: John Wiley & Sons Ltd.

    Google Scholar 

  • Gennetian, L.A., P.A. Morris, J.M. Bos and H.S. Bloom (2005). Constructing Instrumental Variables from Experimental Data to Explore How Treatments Produce Effects. In: Learning More from Social Experiments, ed. Howard S. Bloom. New York: Russell Sage Foundation.

    Google Scholar 

  • Gieryn, T.F. (1999). Cultural Boundaries of Science: Credibility on the Line. Chicago: The University of Chicago Press.

    Google Scholar 

  • Heckman, J.J. (2001). Micro Data, Heterogeneity, and the Evaluation of Public Policy: Nobel Lecture. Journal of Political Economy 109(4): 673–748.

    Article  Google Scholar 

  • Heckman, J.J. (2005a). The Scientific Model of Causality. In: Sociological Methodology 2005, ed. Ross M. Stolzenberg. Boston, MA: Blackwell Publishing.

    Google Scholar 

  • Heckman, J.J. (2005b). Rejoinder: Response to Sobel. In: Sociological Methodology 2005, ed. Ross M. Stolzenberg. Boston, MA: Blackwell Publishing.

    Google Scholar 

  • Holland, P.W. (1986). Statistics and Causal Inference. Journal of the American Statistical Association 81(396): 945–960.

    Article  Google Scholar 

  • Kahn, J.R. and W.M. Mason. (1987). Political Alienation, Cohort Size, and the Easterlin Hypothesis. American Sociological Review 52(1): 155–69.

    Article  Google Scholar 

  • Keyfitz, N. (1977). What Difference Would It Make if Cancer Were Eradicated? An Examination of the Taeuber Paradox. Demography 14(4): 411–418.

    Article  Google Scholar 

  • Keyfitz, N. and G.S. Littman (1979). Mortality in a Heterogeneous Population. Population Studies 33(2): 333–342.

    Article  Google Scholar 

  • Kish, L. (1987). Statistical Design for Research. New York: John Wiley & Sons.

    Book  Google Scholar 

  • Manton, K.G. and S.S. Poss (1979). Effects of Dependency Among Causes of Death for Cause Elimination Life Table Strategies. Demography 16(2): 313–327.

    Article  Google Scholar 

  • Marx, K. and F. Engels (1976) [1888, 1848]. Manifesto of the Communist Party, pp. 476–519 in their Collected Works, Volume 6, translated by Samuel Moore. New York: International Publishers.

    Google Scholar 

  • Mason, K.O., W.M. Mason, H.H. Winsborough and W.K. Poole (1973). Some Methodological Issues in Cohort Analysis of Archival Data. American Sociological Review 38(2): 242–258.

    Article  Google Scholar 

  • Moffitt, R. (2003). Causal Analysis in Population Research: An Economist’s Perspective. Population and Development Review 29(3): 448–458.

    Article  Google Scholar 

  • Moffitt, R. (2005). Remarks on the Analysis of Causal Relationships in Population Research. Demography 42(1): 91–108.

    Article  Google Scholar 

  • Moffitt, R. (2009). Issues in the Estimation of Causal Effects in Population Research, with an Application to the Effects of Teenage Childbearing. In: Causal Analysis in Population Studies: Concepts, Methods, Applications, eds. H. Engelhardt, A. Prskawetz and H.-P. Kohler. Ort: Verlag.

    Google Scholar 

  • Morgan, S.P. and S.M. Lynch (2001). Success and Future of Demography: The Role of Data and Methods. Annals of the New York Academy of Sciences 954: 35–51.

    Article  Google Scholar 

  • Morgan, S.L. and C. Winship (2007). Counterfactuals and Causal Inference: Methods and Principles for Social Research. New York: Cambridge University Press.

    Google Scholar 

  • Namboodiri, K. (1991). Demographic Analysis: A Stochastic Approach. San Diego, CA: Academic Press, Inc.

    Google Scholar 

  • NìBhrolcháin, M. and T. Dyson (2007). On Causation in Demography: Issues and Illustrations. Population and Development Review 33(1): 1–36.

    Article  Google Scholar 

  • Pearl, J. (2000). Causality: Models, Reasoning, and Inference. New York: Cambridge University Press.

    Google Scholar 

  • Preston, S.H. (1993). The Contours of Demography: Estimates and Projections. Demography 30(4): 593–606.

    Article  Google Scholar 

  • Preston, S.H., P. Heuveline and M. Guillot (2001). Demography: Measuring and Modeling Popuiation Processes. Malden, MA: Blackwell Publishers Inc.

    Google Scholar 

  • Rossi, P.H., R.A. Berk and K.J. Lenihan (1980). Money, Work, and Crime: Experimental Evidence. New York: Academic Press.

    Google Scholar 

  • Rossi, P.H., R.A. Berk and K.J. Lenihan (1982). Saying It Wrong with Figures: A Comment on Zeisel. American Journal of Sociology 88(2): 390–393.

    Article  Google Scholar 

  • Rubin, D.B. (1974). Estimating Causal Effects of Treatments in Randomized and Nonrandomized Studies. Journal of Educational Psychology 66(5): 688–701.

    Article  Google Scholar 

  • Rubin, D.B. (1990). Comment: Neyman (1923) and Causal Inference in Experiments and Observational Studies. Statistical Science 5(4): 472–480.

    Google Scholar 

  • Rubin, D.B. (2004). Direct and Indirect Causal Effects via Potential Outcomes. Scandinavian Journal of Statistics 31(2): 161–170.

    Article  Google Scholar 

  • Smith, H.L. (1990). Specification Problems in Experimental and Nonexperimental Social Research. In: Sociological Methodology 1990, ed. Clifford C. Clogg. Cambridge, MA: Basil Blackwell.

    Google Scholar 

  • Smith, H.L. (1997). Matching with Multiple Controls to Estimate Treatment Effects in Observational Studies. In: Sociological Methodology 1997, ed. Adrian E. Raftery. Oxford, England: Basil Blackwell.

    Google Scholar 

  • Smith, H.L. (2003). Some Thoughts on Causation as It Relates to Demography and Population Studies. Population and Development Review 29(3): 459–469.

    Article  Google Scholar 

  • Smith, H.L. (2005). Introducing New Contraceptives in Rural China: A Field Experiment. Annals of the American Academy of Political and Social Science 599: 246–271.

    Article  Google Scholar 

  • Smith, H.L. (2008). Advances in Age-Period-Cohort Analysis. Sociological Methods & Research 36(3): 287–296.

    Article  Google Scholar 

  • Sobel, M.E. (2005). Discussion: ‘The Scientific Model of Causality’. In: Sociological Methodology 2005, ed. R.M. Stolzenberg. Boston, MA: Blackwell Publishing.

    Google Scholar 

  • Vaupel, J.W., K.G. Manton and E. Stallard (1979). The Impact of Heterogeneity in Individual Frailty on the Dynamics of Mortality. Demography 16(3): 439–454.

    Article  Google Scholar 

  • Webster, R. (1995). Why Freud Was Wrong: Sin, Science, and Psychoanalysis. New York: Basic Books.

    Google Scholar 

  • Winship, C. and D.J. Harding (2008). A Mechanism-Based Approach to the Identification of Age–Period–Cohort Models. Sociological Methods & Research 36(3): 362–401.

    Article  Google Scholar 

  • Winship, C. and S.L. Morgan (1999). The Estimation of Causal Effects from Observational Data. Annual Review of Sociology 25: 659–706.

    Article  Google Scholar 

  • Winship, C. and M. Sobel (2004). Causal Inference in Sociological Studies. In: Handbook of Data Analysis, eds. M.A. Hardy and A. Bryman. Thousand Oaks, CA: Sage Publications Inc.

    Google Scholar 

  • Zeisel, H. (1982a). Disagreement over the Evaluation of a Controlled Experiment. American Journal of Sociology 88(2): 378–389.

    Article  Google Scholar 

  • Zeisel, H. (1982b). Hans Zeisel Concludes the Debate. American Journal of Sociology 88(2): 394–396.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Herbert L. Smith .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Springer Science+Business Media B.V.

About this chapter

Cite this chapter

Smith, H.L. (2009). Causation and Its Discontents. In: Engelhardt, H., Kohler, HP., Fürnkranz-Prskawetz, A. (eds) Causal Analysis in Population Studies. The Springer Series on Demographic Methods and Population Analysis, vol 23. Springer, Dordrecht. https://doi.org/10.1007/978-1-4020-9967-0_10

Download citation

Publish with us

Policies and ethics