Adding LLM-generated, uncertainty-targeted semantic representations — split into assignment and heterogeneity channels and routed asymmetrically — improves finite-sample CATE estimates for most of ten neural host learners across four benchmarks.
Treatment effect estimation with dis- entangled latent factors.Proceedings of the AAAI Conference on Artificial Intel- ligence, 35(12):10923–10930, May 2021
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Uncertainty-Guided LLM Semantic Augmentation for Heterogeneous Treatment Effect Estimation
Adding LLM-generated, uncertainty-targeted semantic representations — split into assignment and heterogeneity channels and routed asymmetrically — improves finite-sample CATE estimates for most of ten neural host learners across four benchmarks.