Cross-Validation and Information Criteria in Causal Modeling

Abstract

Many applications of causal modeling in marketing involve selection among several competing causal models. The author investigates whether common criteria for model selection such as cross-validation indices and information criteria are likely to lead to discovery of the correct population model. Guidance on the use of these selection criteria in practice is provided for substantive marketing researchers. Results indicate that the adequacy of cross-validation depends critically on the method used for sample-splitting. The author suggests the application of Snee's DUPLEX algorithm in this context. For situations in which the assumption of multinormally distributed variables is justified, information criteria are found to be highly appropriate for model selection, outperforming cross-validation methods in several respects.