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PRODID:-//pretalx//cfp.pydata.org//pydataglobal2025//speaker//Z7XLKH
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UID:pretalx-pydataglobal2025-VY398A@cfp.pydata.org
DTSTART:20251211T170000Z
DTEND:20251211T173000Z
DESCRIPTION:Most ML models excel at prediction\, answering questions like _
 "Who will buy our product?"_ or _"Which customers are likely to churn?"_. 
 But when it comes to making actionable decisions\, prediction alone can be
  misleading. Correlation does not imply causation\, and business decisions
  require understanding causal relationships to drive the right outcomes.\n
 \nIn this talk\, we will explore how causal machine learning\, specificall
 y uplift modeling\, can bridge the gap between prediction and decision mak
 ing. Using a real-world use case\, we will showcase how uplift modeling he
 lps identify who will respond positively to interventions while avoiding t
 hose who they might deter.
DTSTAMP:20260719T010339Z
LOCATION:Analytics\, Visualization & Decision Science
SUMMARY:Beyond Just Prediction: Causal Thinking in Machine Learning - Avik 
 Basu
URL:https://cfp.pydata.org/pydataglobal2025/talk/VY398A/
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