%0 Journal Article
%T Big Data, Demography, and Causality
%A Guillaume Wunsch
%A Catherine Gourbin
%A Federica Russo
%J Open Journal of Social Sciences
%P 181-206
%@ 2327-5960
%D 2024
%I Scientific Research Publishing
%R 10.4236/jss.2024.121012
%X The
objectives of this paper </span></span><span><span><span style=\"font-family:&quot;\">are</span></span></span><span><span><span style=\"font-family:&quot;\"> 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.
%K Big Data
%K Demography
%K Causality
%K Abduction
%K Deduction
%K Induction
%U http://www.scirp.org/journal/PaperInformation.aspx?PaperID=130673