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UID:pretalx-pyconde-pydata-2024-KXU7Q8@pretalx.com
DTSTART;TZID=CET:20240423T110500
DTEND;TZID=CET:20240423T113500
DESCRIPTION:We demonstrate a range of different approaches to missing data 
 imputation in employee engagement survey data. Contrasting frequentist sty
 le full-information maximum likelihood approaches with more direct Bayesia
 n imputation and chained equation methods\, we highlight how the different
  assumptions regarding the missing-data license different inferences about
  the imputed values and ultimately the plausible causal narratives which c
 an be expressed in PyMC. In particular we avail of the hierarchical nature
  of employee engagement data to justify a hierarchical approach to justify
 ing the (MAR) missing-at-random assumption for imputation schemes in Peopl
 e Analytics.
DTSTAMP:20260609T150432Z
LOCATION:B09
SUMMARY:Missing Data\, Bayesian Imputation and People Analytics with PyMC -
  Nathaniel Forde
URL:https://pretalx.com/pyconde-pydata-2024/talk/KXU7Q8/
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