Pretraining the R-learner by using the outcome model's active set to weight penalties in the CATE lasso reduces error and raises power when prognostic and predictive factors share support.
(2006), ‘The adaptive lasso and its oracle properties’,Journal of the American statistical association101(476), 1418–1429
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Statistical Learning for Heterogeneous Treatment Effects: Pretraining, Prognosis, and Prediction
Pretraining the R-learner by using the outcome model's active set to weight penalties in the CATE lasso reduces error and raises power when prognostic and predictive factors share support.