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SUMMARY:Differential Adherence and Differential Treatment Response - David
  DeGarmo (University of Oregon)
DTSTART:20260126T133000Z
DTEND:20260126T143000Z
UID:TALK241519@talks.cam.ac.uk
DESCRIPTION:This presentation focuses on practical applications of machine
  learning approaches to better understand (a) heterogeneity of adherence\,
  per-protocol causal estimates\, and (b) heterogeneity of treatment respon
 se causal effects (Weissler et al.\, 2021). Specifically\, discussion on t
 he development of single training models and meta-training models are high
 lighted for their improved statistical efficiency and less stringent model
  assumptions compared to more traditional approaches.\nFor differential ad
 herence\, classic training approaches such as complier average causal effe
 cts (CACE) (Jo et al.\, 2008) to evolving meta-learning algorithms are pre
 sented (Zhong et al.\, 2022). For differential response\, classic training
  models applied in adaptive intervention designs (DeGarmo & Gewirtz\, 2019
 ) to evolving doubly-robust estimator meta-learner algorithms are discusse
 d for conditional average treatment effects (CATE) (Hamaya et al.\, 2025).
  Basic applications are illustrated\; sample data and R code are provided.
LOCATION:Seminar Room 1\, Newton Institute
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