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X-WR-CALNAME;VALUE=TEXT:TSE
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TZID:Europe/Paris
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DTSTART:20241027T030000
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RDATE:20251026T030000
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UID:calendar.135690.field_date.0@www.tse-fr.eu
DTSTAMP:20260911T104317Z
CREATED:20240924T121001Z
DESCRIPTION:Julie Josse (INSERM Montpellier\;INRIA - Université de Montpell
 ier)\, “Measuring and Predicting Treatment Effects: Multi-Source Data\, Ge
 neralization\, and Personalization”\, MAD-Stat. Seminar\, Toulouse: TSE\, 
 May 15\, 2025\, 11:00–12:15\, room Auditorium 5.\n\nIn this talk\, we will
  explore various techniques for estimating treatment effects by leveraging
  different causal measures and integrating multiple data sources\, includi
 ng randomized controlled trials (RCTs) and real-world observational data.
 \nWe will begin by discussing generalization methods that combine RCT data
  with observational datasets to predict treatment effects in populations d
 ifferent from those studied in the trial. We will then examine how the cho
 ice of causal measures (e.g.\, Risk Ratio\, Odds Ratio) affects the validi
 ty and robustness of these generalizations.\nNext\, we will address scenar
 ios where multiple clinical trials are available and explore how causal fe
 derated learning can be used to aggregate evidence across these sources.
DTSTART;TZID=Europe/Paris:20250515T120000
DTEND;TZID=Europe/Paris:20250515T131500
LAST-MODIFIED:20251022T001001Z
LOCATION:Toulouse: TSE\, May 15\, 2025\, 11:00–12:15\, room Auditorium 5
SUMMARY:MAD-Stat. Seminar
URL;TYPE=URI:https://www.tse-fr.eu/seminars/2025-measuring-and-predicting-t
 reatment-effects-multi-source-data-generalization-and-personalization
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