Models of causal inference: Imperfect but applicable is better than perfect but inapplicable
批评了因果图模型在战略管理中的适用性，指出其关键假设在资源基础观中常被违反，并引入向量空间模型作为定量替代方法，以改进因果推断。
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.