posted on 2020-05-15, 08:58authored byRoss Macmillan, Carmel Hannan
Recent decades have seen renewed attention to issues of causal inference in the social
sciences, yet implications for life course research have not been spelled out nor is it clear
what types of approaches are best suited for theoretical development on life course
processes. We begin by evaluating a number of meta-theoretical perspectives, including
critical realism, data mining and experimentation, and find them limited in their potential for causal claims in a life course context. From this, we initiate a discussion of the logic and practice of ‘natural experiments’ for life course research, highlighting issues of how to
identify natural experiments, how to use cohort information and variation in the order and
timing of life course transitions to isolate variation in exposure, how such events that alter
social structures are the key to identification in causal processes of the life course and,
finally, of analytic strategies for the extraction of causal conclusions from conventional
statistical estimates. Through discussion of both positive and negative examples, we
outline the key methodological issues in play and provide a road map of best practices.
While we acknowledge that causal claims are not necessary for social explanation, our
goal is to explain how causal inference can benefit life course scholarship and outline a
set of practices that can complement conventional approaches in the pursuit of causal
explanation in life course research.
History
Publication
Longitudinal and Life Course Studies;11(1), pp. 7–25