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Causality in Social Network Analysis

Author

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  • PATRICK DOREIAN

    (University of Pittsburgh)

Abstract

The role that causality can play in social network analysis is unclear. The author provides a broad characterization of social network analysis before considering the nature of causality. He distinguishes four types of causality: system causality, statistical causality, mechanism causality, and algorithmic causality. Their potential places in network analysis are discussed. Understanding generative mechanisms—be they system, mechanism, or algorithmic—seems the most promising way to proceed. The role of statistical causality is a source of potential data analytic tools that can be mobilized within analyses conducted in the spirit of the other three types of causality.

Suggested Citation

  • Patrick Doreian, 2001. "Causality in Social Network Analysis," Sociological Methods & Research, , vol. 30(1), pages 81-114, August.
    • Handle: RePEc:sae:somere:v:30:y:2001:i:1:p:81-114
    DOI: 10.1177/0049124101030001005
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    References listed on IDEAS

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    1. Elster,Jon, 1983. "Explaining Technical Change," Cambridge Books, Cambridge University Press, number 9780521270724.
    2. Stanley Wasserman & Philippa Pattison, 1996. "Logit models and logistic regressions for social networks: I. An introduction to Markov graphs andp," Psychometrika, Springer;The Psychometric Society, vol. 61(3), pages 401-425, September.
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    Cited by:

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    2. Bruno Arpino & Luca De Benedictis & Alessandra Mattei, 2017. "Implementing propensity score matching with network data: the effect of the General Agreement on Tariffs and Trade on bilateral trade," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 66(3), pages 537-554, April.

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