Agent‐based Models and Causal Inference

Bibliographic Data

ID23774673
EditorsGianluca Manzo
Year2022
Pages256
Publication date2022-02-09
Open AccessYes
TypeBOOK
VenueAgent-Based Models and Causal Inference (SOURCE_BOOK)
PublisherWiley (PUBLISHER • GB)
DOI10.1002/9781119704492
OpenAlexW4213347363
Open LibraryOL33828204M
ISBN9781119704492
LanguageEN
Citations received5

"The concept of causality and quantitative techniques for causal inference have been extensively discussed in sociology by a variety of scholars with different research programs and theoretical perspectives (see, among others, Marini and Singer 1988; Abbott 1998; Doreian 1999; Goldthorpe 2001; Winship and Sobel 2004; Mahoney 2008; Gangl 2010; Mahoney and Ragin 2013; for a historical perspective, see also Barringer et al. 2013). Among quantitative sociologists, the interest in causality issues was reinforced by the rapid diffusion in sociology of the potential outcome approach to causality (Morgan and Winship 2014), an approach in turn fostered by older contributions in statistics (for a historical overview, see Imbens and Rubin 2015 ch. 2) and economics (Heckman 2005), and reinvigorated by recent developments in computer science (Spirtes et al. 2000; Pearl 2009) as well as philosophical discussions (Woodward 2003). Extensive literature review documented that establishing causal claims is now one of the primary goals of many articles published in leading sociological journals, be these quantitative, qualitative or historical studies (see Abend et al. 2013; Ermakoff 2019). Traces of the concept of causality can also be found in the literature on mechanism-based thinking (Hedström and Ylikoski 2010) and on agent-based computational models (Bianchi and Squazzoni 2015). And these also are two research topics in rapid expansion in contemporary sociology"

Biology · Causal inference · Computational biology · Econometrics · Inference · Artificial Intelligence · Computer Science · Mathematics · Philosophy and History of Science

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