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UID:pretalx-cfp-8QNFLU@eindhoven2024.pydata.org
DTSTART;TZID=Europe/Amsterdam:20240711T160500
DTEND;TZID=Europe/Amsterdam:20240711T163500
DESCRIPTION:A lot of industry-available Machine Learning solutions for caus
 al forecasting have a very particular blind spot: unobserved confounders. 
 We will present an approach that allows you to combine state-of-the-art Ma
 chine Learning approaches with advanced Econometrics techniques to get the
  better of both worlds: accurate causal inference and good forecasting acc
 uracy.
DTSTAMP:20250709T215749Z
LOCATION:Else (1.3)
SUMMARY:Causal Forecasting: How to disentangle causal effects\, while contr
 olling for unobserved confounders and keeping accuracy - Marc Nientker
URL:https://eindhoven2024.pydata.org/cfp/talk/8QNFLU/
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