Abstract:
:The relative weights of empirical facts (data) and assumptions (theory) in causal inference vary across disciplines. Typically, disciplines that ask more complex questions tend to better tolerate a greater role of theory and modeling in causal inference. As epidemiologists move toward increasingly complex questions, Marshall and Galea (Am J Epidemiol. 2015;181(2):92-99) support a reweighting of data and theory in epidemiologic research via the use of agent-based modeling. The parametric g-formula can be viewed as an intermediate step between traditional epidemiologic methods and agent-based modeling and therefore is a method that can ease the transition toward epidemiologic methods that rely heavily on modeling.
journal_name
Am J Epidemioljournal_title
American journal of epidemiologyauthors
Hernán MAdoi
10.1093/aje/kwu272subject
Has Abstractpub_date
2015-01-15 00:00:00pages
103-5issue
2eissn
0002-9262issn
1476-6256pii
kwu272journal_volume
181pub_type
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