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Causality in the Social Sciences: a structural modelling framework

Author

Listed:
  • Federica Russo

    (University of Amsterdam)

  • Guillaume Wunsch

    (University of Louvain (UCLouvain))

  • Michel Mouchart

    (University of Louvain (UCLouvain))

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.

Suggested Citation

  • Federica Russo & Guillaume Wunsch & Michel Mouchart, 2019. "Causality in the Social Sciences: a structural modelling framework," Quality & Quantity: International Journal of Methodology, Springer, vol. 53(5), pages 2575-2588, September.
    • Handle: RePEc:spr:qualqt:v:53:y:2019:i:5:d:10.1007_s11135-019-00872-y
    DOI: 10.1007/s11135-019-00872-y
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    References listed on IDEAS

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    1. Wunsch, Guillaume & Mouchart, Michel & Russo, Federica, 2014. "Functions and Mechanisms in Structural-Modelling Explanations," LIDAM Reprints ISBA 2014011, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    2. Russo, Federica & Wunsch, Guillaume & Mouchart, Michel, 2011. "Inferring causality through counterfactuals in observational studies - Some epistemological issues," LIDAM Reprints ISBA 2011014, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    3. Michel MOUCHART & Guillaume WUNSCH & Federica RUSSO, 2016. "Controlling Variables in Social Syqtems - A Structural Modelling Approach," LIDAM Reprints CORE 2761, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    4. Michel Mouchart & Federica Russo & Guillaume Wunsch, 2010. "Inferrig Causal Relations by Modelling Structure," Statistica, Department of Statistics, University of Bologna, vol. 70(4), pages 411-432.
    5. Wunsch, Guillaume & Mouchart, Michel & Russo, Federica, 2010. "Do we necessarily need longitudinal data to infer causal relations?," LIDAM Reprints ISBA 2010016, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    6. Mouchart, Michel & Wunsch, Guillaume & Russo, Federica, 2016. "Controlling Variables in Social Systems - A Structural Modelling Approach," LIDAM Reprints ISBA 2016038, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    7. Giorgio Fagiolo & Alessio Moneta & Paul Windrum, 2007. "A Critical Guide to Empirical Validation of Agent-Based Models in Economics: Methodologies, Procedures, and Open Problems," Computational Economics, Springer;Society for Computational Economics, vol. 30(3), pages 195-226, October.
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    Cited by:

    1. Guillaume Wunsch & Federica Russo & Michel Mouchart & Renzo Orsi, 2020. "Time and Causality in the Social Sciences," Working Papers wp1155, Dipartimento Scienze Economiche, Universita' di Bologna.
    2. M. Mouchart & R. Orsi & G. Wunsch, 2020. "Causality in Econometric Modeling. From Theory to Structural Causal Modeling," Working Papers wp1143, Dipartimento Scienze Economiche, Universita' di Bologna.

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