Abstract
Models which aim to represent causal relationships, and that can be used for predictive purposes, are becoming more widespread in the business community. A model might be built that explores whether a consumer will buy broadband, or what drives dissatisfaction. Then these models can be tested and used to communicate objectives or for business decisions such as rational setting of targets or what should form a change initiative. This paper investigates why creating a model that captures causal drivers within the system is far more challenging than producing a model that fits, or explains, the data well. Concepts used within causal modelling are introduced and one particular technique, structural equation modelling, is explored. This paper is kept non-mathematical and does not require statistical expertise. A real example of a model is used to demonstrate its application.
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Elliott, M.R. Causality and How to Model It. BT Technology Journal 21, 120–125 (2003). https://doi.org/10.1023/A:1024447121042
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DOI: https://doi.org/10.1023/A:1024447121042