Abstract
Over the last two decades the analytical toolbox for examining the properties needed to claim causal relationships has been significantly extended. New approaches to the theory of causality rely on the concept of ‘intervention’ instead of ‘association’. Under an axiomatic framework they elaborate the conditions for safe causal inference from nonexperimental data. Inferred Causation Theory (Spirtes et. al., 2000; Pearl, 2000) teaches us that the same independence relationships (or covariance matrix) may have been generated by numerous other graphs representing the cause-effect hypotheses. ICT combines elements of graph theory, statistics, logic, and computer science. It is not limited to parametric models in need of quantitative (ratio or interval scaled) data, but also operates much more generally on the observed conditional independence relationships among a set of qualitative (categorical) observations. Causal inference does not appear to be restricted to experimental data. This is particularly promising for research domains such as consumer behavior where policy makers and managers are unwilling to engage in experiments on real markets. A case example highlights the potential use of Inferred Causation methodology for analyzing the marketing researchers’ belief systems about their scientific orientation.
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Mazanec, J.A. (2006). Inferred Causation Theory: Time for a Paradigm Shift in Marketing Science?. In: Spiliopoulou, M., Kruse, R., Borgelt, C., Nürnberger, A., Gaul, W. (eds) From Data and Information Analysis to Knowledge Engineering. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-31314-1_5
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