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Models of causal inference: Imperfect but applicable is better than perfect but inapplicable
Author(s) -
Ellsaesser Florian,
Tsang Eric W. K.,
Runde Jochen
Publication year - 2014
Publication title -
strategic management journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 11.035
H-Index - 286
eISSN - 1097-0266
pISSN - 0143-2095
DOI - 10.1002/smj.2164
Subject(s) - causal inference , imperfect , inference , causal model , computer science , graph , resource (disambiguation) , focus (optics) , management science , key (lock) , mathematical economics , econometrics , artificial intelligence , theoretical computer science , economics , mathematics , linguistics , philosophy , statistics , computer network , physics , computer security , optics
We assess a recent paper by Durand and Vaara (2009) that advances causal graph modeling as a tool for inferring causes in strategy research. We focus on the M arkov condition, a key assumption on which causal graph modeling is based, and show why this condition is invariably violated in strategic management in general and the resource‐based view of the firm in particular. We then introduce vector space modeling as a quantitative alternative to causal graph modeling, and consider how improved methods of causal inference might enhance our ability to test some of the central propositions of the resource‐based view . Copyright © 2013 John Wiley & Sons, Ltd.