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On Weighted Orthogonal Learners for Heterogeneous Treatment Effects
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Motivated by applications in personalized medicine and individualized policymaking, there is a growing interest in techniques for quantifying treatment effect heterogeneity in terms of the conditional average treatment effect (CATE). Some of the most prominent methods for CATE estimation developed in recent years are T-Learner, DR-Learner and R-Learner. The latter two were designed to improve on the former by being Neyman-orthogonal. However, the relations between them remain unclear, and likewise the literature remains vague on whether these learners converge to a useful quantity or (functional) estimand when the underlying optimization procedure is restricted to a class of functions that does not include the CATE. In this article, we provide insight into these questions by discussing DR-Learner and R-Learner as special cases of a general class of weighted Neyman-orthogonal learners for the CATE, for which we moreover derive oracle bounds. Our results shed light on how one may construct Neyman-orthogonal learners with desirable properties, on when DR-Learner may be preferred over R-Learner (and vice versa), and on novel learners that may sometimes be preferable to either of these. Theoretical findings are confirmed using results from simulation studies on synthetic data, as well as an application in critical care medicine.
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Cited by 7 Pith papers
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A New Targeted-Federated Learning Framework for Estimating Heterogeneity of Treatment Effects: A Robust Framework with Applications in Aging Cohorts
A targeted federated estimator for heterogeneous treatment effects, combining doubly robust scores with density-ratio weighting and a bootstrap source-selection step.
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Treatment Effect Estimation for Optimal Decision-Making
The paper introduces a tunable, policy-targeted CATE estimator (PT-CATE) that trades prediction error against decision performance, though its central suboptimality proof is incomplete.
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Orthogonal Survival Learners for Estimating Heterogeneous Treatment Effects from Time-to-Event Data
The paper proposes a general toolbox of orthogonal survival learners with custom weighting functions to estimate heterogeneous treatment effects robustly under treatment, censoring, and survival overlap violations.
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Latent Variable Modeling for Robust Causal Effect Estimation
Latent DML fits a parametric latent variable model to DML residuals and adjusts the outcome residual before the final effect regression, yielding consistent estimates under well-specified unobserved confounding.
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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.
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Causal machine learning for heterogeneous treatment effects in the presence of missing outcome data
The authors extend the DR-learner and EP-learner to handle outcomes missing at random by adding inverse-probability-of-censoring weights, and show the resulting estimators are oracle efficient.
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