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Causality in the Social Sciences: a structural modelling framework

  • Federica Russo
  • Guillaume Wunsch
  • Michel MouchartEmail author
Article
  • 4 Downloads

Abstract

There is no unified theory of causality in the sciences and in philosophy. In this paper, we focus on a particular framework, called structural causal modelling (SCM), as one possible perspective in quantitative social science research. We explain how this methodology provides a fruitful basis for causal analysis in social research, for hypothesising, modelling, and testing explanatory mechanisms. This framework is not based on a system of equations, but on an analysis of multivariate distributions. In particular, the modelling stage is essentially distribution-free. Adopting an SCM approach means endorsing a particular view on modelling in general (the hypothetico-deductive methodology), and a specific stance on exogeneity (namely as a condition of separability of inference), on the one hand, and in interpreting marginal–conditional decompositions (namely as mechanisms), on the other hand.

Keywords

Structural causal modelling Recursive decomposition Mechanisms Causality - Causal modelling 

Notes

Acknowledgements

Comments, in particular by Catherine Gourbin, Renzo Orsi, and Frans Willekens, on former versions of this paper, are gratefully acknowledged.

References

  1. Fagiolo, G., Moneta, A., Windrum, P.: A critical guide to empirical validation of agent-based models in economics: methodologies, procedures, and open problems. Comput. Econ. 30, 195–226 (2007)CrossRefGoogle Scholar
  2. Gourbin, C., Wunsch, G., Moreau, L., Guillaume, A.: Direct and indirect paths leading to contraceptive use in urban Africa. An application to Burkina Faso, Ghana, Morocco and Senegal. Rev. Quetelet/Quetelet J. 5(1), 33–70 (2017)Google Scholar
  3. Hood, W.C., Koopmans, T.C. (eds.): Studies in econometric methods, Cowles Foundation Monograph 14. Wiley, New-York (1953)Google Scholar
  4. Illari, P., Russo, F.: Causality: philosophical theory meets scientific practice. Oxford University Press, Oxford (2014)Google Scholar
  5. Johnson, R.B., Russo, F., Schoonenboom, J.: Causation in mixed methods research: the meeting of philosophy, science, and practice. J. Mixed Methods Res. (2017).  https://doi.org/10.1177/1558689817719610 Google Scholar
  6. Koopmans, T.C.: Measurement without theory. Rev. Econ. Stat. 29, 161–173 (1947)CrossRefGoogle Scholar
  7. Koopmans, T.C. (ed.): Statistical inference in dynamic economic models, Cowles Foundation Monograph 10. Wiley, New York (1950)Google Scholar
  8. Little, D.: Levels of the social. In: Risjord, M., Turner, S. (eds.) Philosophy of anthropology and sociology, pp. 343–371. Elsevier Science, Amsterdam (2006)Google Scholar
  9. Mackie, J.L.: Causes and conditions. Am. Philos. Q. 2(4), 245–264 (1965)Google Scholar
  10. Morgan, S.L., Winship, C.: Counterfactuals and causal inference. Cambridge University Press, Cambridge (2007)CrossRefGoogle Scholar
  11. Mouchart, M., Orsi, R.: Building a structural model: parameterization and structurality. Econometrics 4, 23 (2016).  https://doi.org/10.3390/econometrics4020023 CrossRefGoogle Scholar
  12. Mouchart, M., Russo, F.: Causal explanation: recursive decompositions and mechanisms, chap. 15. In: McKay Illari, P., Russo, F., Williamson, J. (eds.) Causality in the sciences, pp. 317–337. Oxford University Press, Oxford (2011)CrossRefGoogle Scholar
  13. Mouchart, M., Russo, F., Wunsch, G.: Structural modelling, exogeneity, and causality, Chapter 4. In: Engelhardt, H., Kohler, H.-P., Fürnkranz-Prskawetz, A. (eds.) Causal analysis in population studies: concepts, methods, applications, pp. 59–82. Springer, Dordrecht (2009)CrossRefGoogle Scholar
  14. Mouchart, M., Russo, F., Wunsch, G.: Inferring causal relations by modelling structures. Statistica LXX(4), 411–432 (2010)Google Scholar
  15. Mouchart, M., Wunsch, G., Russo, F.: Controlling variables in social systems: a structural modelling approach. Bull. Sociol. Methodol. 132, 5–25 (2016)CrossRefGoogle Scholar
  16. Pearl, J.: Causality. Models, reasoning, and inference. Cambridge University Press, Cambridge (2000). (revised and enlarged in 2009) Google Scholar
  17. Russo, F.: Causality and causal modelling in the social sciences: measuring variations, Methodos Series, vol. 5. Springer, Berlin (2009)CrossRefGoogle Scholar
  18. Russo, F., Wunsch, G., Mouchart, M.: Inferring causality through counterfactuals in observational studies. Some epistemological issues. Bull. Sociol. Methodol. 111, 43–64 (2011)CrossRefGoogle Scholar
  19. Wold, H.O.: Causality and econometrics. Econometrica 22(2), 162–177 (1954)CrossRefGoogle Scholar
  20. Wunsch, G., Mouchart, M., Russo, F.: Functions and mechanisms in structural-modelling explanations. J. Gen. Philos. Sci. 45(1), 187–208 (2014)CrossRefGoogle Scholar
  21. Wunsch, G., Mouchart, M., Russo, F.: Causal attribution in block-recursive systems: a structural modelling perspective. Methodol. Innov. (2018).  https://doi.org/10.1177/2059799118768415 Google Scholar
  22. Wunsch, G., Russo, F., Mouchart, M.: Do we necessarily need longitudinal data to infer causal relations? Bull. Sociol. Methodol. 106, 5–18 (2010)CrossRefGoogle Scholar

Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.Department of PhilosophyUniversity of AmsterdamAmsterdamThe Netherlands
  2. 2.Center for Demographic ResearchUniversity of Louvain (UCLouvain)Louvain-la-NeuveBelgium
  3. 3.Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA)University of Louvain (UCLouvain)Louvain-la-NeuveBelgium

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