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UID:pretalx-pyconde-pydata-2026-Q9DU8N@pretalx.com
DTSTART;TZID=CET:20260415T113500
DTEND;TZID=CET:20260415T122000
DESCRIPTION:Causal inference asks the hardest question in data science: "Wh
 at would have happened if things were different?" While traditional method
 s often rely on rigid rules\, statistical tests or "black box" adjustments
 \, Probabilistic Programming Languages (PPLs) like PyMC and NumPyro offer 
 a transparent\, flexible\, and powerful lens to view these problems.\n\nIn
  this talk\, we move beyond the standard "correlation is not causation" di
 sclaimer. We will build a unified workflow that starts with robust A/B tes
 ting\, moves to bias adjustment in observational data using multilevel mod
 els\, and culminates with advanced Deep Causal Latent Variable Models (CEV
 AE).
DTSTAMP:20260412T141727Z
LOCATION:Helium [3rd Floor]
SUMMARY:Causal Inference through the lens of probabilistic programming - Dr
 . Juan Orduz
URL:https://pretalx.com/pyconde-pydata-2026/talk/Q9DU8N/
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