Benjamin Vincent
Ben Vincent is Director of InferenceWorks Ltd and a Principal Data Scientist at PyMC Labs, where he has been building Bayesian solutions for real-world business problems since 2021. He created CausalPy, an open-source Python library for causal inference in quasi-experimental settings. He holds a PhD in Neuroscience from the University of Sussex (UK) and previously held a university faculty position for 15 years.
Session
Your company launches a loyalty program — but not everywhere at once. Ten stores get it in January, another ten in March, the rest later. Leadership asks: "Did it work? By how much?" You compare before and after... and get a number that's wrong. Phased rollouts break naive pre/post comparisons, and standard regression quietly gives misleading answers.
This talk shows a practical Python workflow for getting it right. Using a realistic store-rollout example and CausalPy (an open-source library), I'll demonstrate how to produce event-study plots that show when and how much an intervention takes effect — with uncertainty estimates your stakeholders can actually act on. Whether you're measuring feature flags, marketing campaigns, or policy changes, you'll leave with a reproducible notebook and a step-by-step workflow you can apply tomorrow.