How to use causal models (DAGs) in your MMM for better insights | How to Build an MMM Ep.2 скачать в хорошем качестве

How to use causal models (DAGs) in your MMM for better insights | How to Build an MMM Ep.2

What’s the secret to building a powerful statistical model? In Episode 2 of How to Build a Media Mix Model (MMM), Tom Vladeck and Antonio Vargas from Recast reveal the importance of causal models and assumptions in MMM. Learn how to: Start with your core assumptions rather than fitting data into a pre-existing model Use Directed Acyclic Graphs (DAGs) to map causal relationships Balance trade-offs in model estimation for actionable results Antonio Vargas, Senior Data Scientist at Recast, explains how their proprietary Bayesian model integrates complex assumptions into a robust causal framework for MMM. Whether you’re using open-source models or building in-house, understanding causal relationships is key to successful modeling. 🖥️ Learn more about Recast's MMM platform: https://getrecast.com/ 📚 Explore Recast's Bayesian model documentation: https://docs.getrecast.com/ 🔔 Subscribe to stay updated with the series.