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UID:409@isdm.umontpellier.fr
DTSTART;TZID=Europe/Paris:20240924T140000
DTEND;TZID=Europe/Paris:20240924T140000
DTSTAMP:20260828T115936Z
URL:https://isdm.umontpellier.fr/events/interpretable-causal-inference-for
 -analyzing-wearable-sensor-and-other-distributional-data-2/
SUMMARY:Interpretable causal inference for analyzing wearable\, sensor\, an
 d other distributional data
DESCRIPTION:Online\n\nMachine Learning in Montpellier\, Theory &amp\; Pract
 ice\n\nMany modern causal questions ask how treatments affect complex outc
 omes that are measured using wearable devices and sensors. Current analysi
 s approaches require summarizing these data into scalar statistics (e.g.\,
  the mean)\, but these summaries can be misleading. For example\, disparat
 e distributions can have the same means\, variances\, and other statistics
 . Researchers can overcome the loss in information by instead representing
  the data as distributions. We develop an interpretable method for distrib
 utional data analysis that ensures trustworthy and robust decision making:
  Analyzing Distributional Data via Matching After Learning to Stretch (ADD
  MALTS). We (i) provide analytical guarantees of the correctness of our es
 timation strategy\,(ii) demonstrate via simulation that ADD MALTS outperfo
 rms other distributional data analysis methods at estimating treatment eff
 ects\, and (iii) illustrate ADD MALTS’ ability to verify whether there i
 s enough cohesion between treatment and control units within subpopulation
 s to trustworthily estimate treatment effects. We demonstrate ADD MALTS’
  utility by studying the effectiveness of continuous glucose monitors in m
 itigating diabetes risks.\n\nMachine Learning in Montpellier\, Theory &amp
 \; Practice
ATTACH;FMTTYPE=image/jpeg:https://isdm.umontpellier.fr/wp-content/uploads/
 2026/06/ml-mtp-gC78d5.png
CATEGORIES:ML MTP
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