In a perfect world, every feature launch is a clean A/B test with a clear winner. In the world of high-growth platforms like Meta, Coinbase, and Loom, launches are messy. Users influence one another through network effects, selection bias skews your metrics, and a simple t-test often hides more than it reveals.
This talk walks through the anatomy of a "Launch Post-Mortem" using a Jupyter Notebook. We will move beyond basic statistical significance to explore how to handle the complexities of real-world growth data.
Attendees will learn how to use Python libraries like CausalML and PyMatch to account for user behavior bias and network interference, ensuring that "winning" features actually drive long-term value.