Highly adaptive Lasso for estimation of heterogeneous treatment effects and treatment recommendation
The estimation of conditional average treatment effects (CATEs) is an important problem in many applications. Many machine learning-based frameworks for such estimation have been proposed, including meta-learning, causal trees, and causal forests. However, few of these methods are interpretable, whi...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
De Gruyter
2025-08-01
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| Series: | Journal of Causal Inference |
| Subjects: | |
| Online Access: | https://doi.org/10.1515/jci-2023-0085 |
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