📖 Read the companion deep dive (with diagrams & checklists) : link
Have you ever spent months building a "perfect" model, only to ship it and see absolutely zero movement in your metrics?
Welcome to the Data Validity Cliff—the point where your observational data is solid for prediction, but completely disappears the moment you try to use it for a business decision. In this episode, we break down Judea Pearl’s Ladder of Causation to explain why your high-tech models are failing in the boardroom and how you can fix them.
In this episode, we discuss:
• The Selection vs. Policy Trap: Why finding users who already love your product (Selection) is not the same as changing user behavior (Policy).
The Three Rungs of Intelligence:
Level 1 (The Owl): Why ML as "superpowered observation" is great for prediction but useless for deciding what to change.
Level 2 (The Baby): How to move from "seeing" to "doing" using A/B testing and interventional data.
Level 3 (The Scientist): The "Road Not Taken"—using structural models to simulate parallel universes and "transport" results from one market (like the UK) to another (like the US).
The "Smell Test" Diagnostic: A 2-step survival checklist to use before you present your next set of results to stakeholders.
Stop being just an observer. Start becoming a simulator.
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