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Complexity, Causality, and Control in Statistical Modeling

Bibliographic Data

ID4779686
AuthorsBrian R Urlacher (0000-0002-8356-4228, University of North Dakota, Grand Forks, ND, USA, corresponding author)
Year2020
Volume64
Issue1
Pages55-73
Publication date2020-01-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueAmerican Behavioral Scientist (JOURNAL)
Journal identifiersISSN: 0002-7642 • E-ISSN: 1552-3381
PublisherSAGE Publications Inc (PUBLISHER)
DOI10.1177/0002764219859641
OpenAlexW2959642935
LanguageEN
References cited38

Social scientists using statistical models and more qualitative techniques frequently employ divergent approaches to thinking about causality. Statistical methodologies tend to draw on probabilistic understandings of causality. Qualitative research traditions, however, have advanced a sophisticated framework around necessary and sufficient conditions. In particular, the qualitative comparative analysis approach has embraced theory development that emphasizes equifinality and complex causal relationships. This article reviews the two traditions and explores how a causal framework grounded in necessary and sufficient conditions can be adapted to statistical models. A logistic regression analysis of major contributions to peacekeeping missions is used to illustrate both the viability of blending the two traditions as well as the potential for more sophisticated theory development and testing

Causal inference · Causal model · Causality (physics) · Data science · Econometrics · Economics · Epistemology · Equifinality · Machine learning · Management science · Probabilistic logic · Qualitative comparative analysis · Statistical hypothesis testing · Statistical model · Statistics · Artificial Intelligence · Computer Science · International Relations and Foreign Policy · Mathematics · Political Conflict and Governance · Qualitative Comparative Analysis Research

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