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UID:pretalx-pydata-london-2026-H7PFXK@pretalx.com
DTSTART;TZID=GMT:20260606T144500
DTEND;TZID=GMT:20260606T153000
DESCRIPTION:Your company launches a loyalty program — but not everywhere 
 at once. Ten stores get it in January\, another ten in March\, the rest la
 ter. Leadership asks: "Did it work? By how much?" You compare before and a
 fter... and get a number that's wrong. Phased rollouts break naive pre/pos
 t comparisons\, and standard regression quietly gives misleading answers.\
 n\nThis 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 *h
 ow 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 reproducibl
 e notebook and a step-by-step workflow you can apply tomorrow.
DTSTAMP:20260602T225332Z
LOCATION:Hardwick Hub
SUMMARY:Did Your Rollout Actually Work? Measuring Phased Launches with Stag
 gered DiD in Python - Benjamin Vincent
URL:https://pretalx.com/pydata-london-2026/talk/H7PFXK/
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