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UID:226@isdm.umontpellier.fr
DTSTART;TZID=Europe/Paris:20250325T140000
DTEND;TZID=Europe/Paris:20250325T140000
DTSTAMP:20260825T133450Z
URL:https://isdm.umontpellier.fr/events/combining-t-learning-and-dr-learni
 ng-a-framework-for-oracle-efficient-estimation-of-causal-contrasts/
SUMMARY:Combining T-learning and DR-learning: a framework for oracle-effici
 ent estimation of causal contrasts
DESCRIPTION:Online\n\nMachine Learning in Montpellier\, Theory &amp\; Pract
 ice\n\nWe introduce efficient plug-in (EP) learning\, a novel framework fo
 r the estimation of heterogeneous causal contrasts\, such as the condition
 al average treatment effect and conditional relative risk. The EP-learning
  framework enjoys the same oracle-efficiency as Neyman-orthogonal learning
  strategies\, such as DR-learning and R-learning\, while addressing some o
 f their primary drawbacks\, including that (i) their practical applicabili
 ty can be hindered by loss function non-convexity\; and (ii) they may suff
 er from poor performance and instability due to inverse probability weight
 ing and pseudo-outcomes that violate bounds. To avoid these drawbacks\, EP
 -learner constructs an efficient plug-in estimator of the population risk 
 function for the causal contrast\, thereby inheriting the stability and ro
 bustness properties of plug-in estimation strategies like T-learning. Unde
 r reasonable conditions\, EP-learners based on empirical risk minimization
  are oracle-efficient\, exhibiting asymptotic equivalence to the minimizer
  of an oracle-efficient one-step debiased estimator of the population risk
  function. In simulation experiments\, we illustrate that EP-learners of t
 he conditional average treatment effect and conditional relative risk outp
 erform state-of-the-art competitors\, including T-learner\, R-learner\, an
 d DR-learner. Open-source implementations of the proposed methods are avai
 lable in our R package hte3.\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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