Resumen de Models of causal inference: : Imperfect but applicable is better than perfect but inapplicable

Florian Ellsaesser, Eric W. K. Tsang, Jochen Runde

• 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 Markov 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.