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Causality, Optimality, and Marketing Modeling

Peter E. Rossi, University of California, Los Angeles

The role of causal inference is to establish the true effect sizes for various marketing actions. As such, causal inference relies heavily on counterfactual reasoning. For example, to understand the true causal effects of ad exposure, we must answer the counterfactual question—what would have happened if customers were not exposed to the ad? Valid causal inferences are necessary to any attempt to optimize marketing actions such as in a marketing mix model. Peter Rossi will review the basics of causal inference and provide examples of valid reasoning and challenges for existing practices.

Related Conference:

Harnessing Marketing Analytics for Business Impact

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