In the era of data-driven decision-making, understanding causal relationships is crucial for effective marketing strategies. This talk delves into the underexplored connection between Bayesian causal thinking and media mix modeling, linking Directed Acyclic Graphs (DAGs), Structural Causal Models (SCMs), and the Data Generation Process (DGP). By navigating through these key concepts, we will demonstrate how we can build models that not only predict outcomes but also represent causal mechanisms within the marketing ecosystem.

Starting from foundational principles, we will explore how DAGs serve as a formal language for encoding causal assumptions, how Structural Causal Modeling define relationships in media mix models, and how we implement those in the Bayesian framework through the famous DGP. We will further illustrate how media mix models can be employed as causal inference tools to estimate counterfactuals and causal effects, providing actionable insights into the effectiveness of media investments.

Finally, we’ll show how Bayesian inference enables us to update these causal beliefs in light of data. This synthesis of causal reasoning and probabilistic modeling is not only theoretically rich but practically powerful—offering a robust framework for constructing media mix models that more accurately reflect the complexities of real-world marketing dynamics.

Attendees will leave with an understanding of how to apply Bayesian causal discovery (guided by an example in an IPython notebook) to develop causally valid models that can be applied to real-world marketing data. They will learn how to use Media Mix Models as causal inference tools to estimate counterfactual scenarios and causal effects, unlocking deeper insights into the effectiveness of media investments. This presentation aims to reveal a new pathway for marketers, data scientists, and researchers to harness the potential of these powerful methodologies together, empowering them to drive more informed, causally grounded decisions.