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Kashyap, R., Rinderknecht, R. G., Akbaritabar, A., Alburez-Guterriez, D., Gil-Clavel S., Grow, A., Kim, J., Leasure, D. R., Lohmann, S., Negraia D. V., Perrotta, D., Rampazzo, F., Tsai, C.-J., Verhagen, M. D., Zagheni, E., & Zhao, X. (2023). Digital and Computational Demography. In J. Skopek (Ed.), Research Handbook on Digital Sociology (pp. 47-85). Edward Elgar Publishing.
https://doi.org/10.4337/9781789906769.00010

has been cited by the following article:

• TITLE: Big Data, Demography, and Causality

  AUTHORS: Guillaume Wunsch, Catherine Gourbin, Federica Russo

  KEYWORDS: Big Data, Demography, Causality, Abduction, Deduction, Induction

  JOURNAL NAME: Open Journal of Social Sciences, Vol.12 No.1, January 24, 2024

  ABSTRACT: The objectives of this paper are to examine to what extent Big Data are presently used in population research and to consider their potential for causal inference. After examining the characteristics and challenges of big data, the subsequent section deals with the use of big data in the study of the key demographic phenomena and is based on a literature review for the period 2015-2022 of 63 scientific journals concerned with population issues. The final section examines to what extent the use of big data could improve causal inference. Our results show that demographers continue to privilege sources of numerical data and are less prone to use digital media data or other sources such as images. Big Data can contribute to improving explanations in demography thanks to the large number of observations and variables in the data sets, especially when they can be individually linked together. Causal knowledge requires however that one can propose and test a suitable mechanism explaining why a variation in one variable produces a variation in another variable.