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Paper Citation Record · LEDGER

Uncertainty-Guided LLM Semantic Augmentation for Heterogeneous Treatment Effect Estimation

As of 6 August 2026, this Paper Citation Record lists 59 of 59 outbound references and 0 inbound Pith citation observations for arXiv:2607.26599.

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pith.paper-citation-record.v1
2607.26599 v1

Coverage vector

measured 59 of 59 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-01T12:41:27.538063Z

measured 59 of 59 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-06T06:34:29.942622+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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Source: cited_works

Reference resolution

59 of 59 outbound references displayed

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Outbound references

Observation 8197bafa-d0d9-4971-a153-6a36baf34176 · outbound

This paper cites Improved algorithms for linear stochastic bandits.

Uncertainty-Guided LLM Semantic Augmentation for Heterogeneous Treatment Effect Estimation Improved algorithms for linear stochastic bandits

Reference 1

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Observation 3f704bc7-b80e-498a-bf17-7b31c5c136b3 · outbound

This paper cites Alaa and Mihaela van der Schaar.

Uncertainty-Guided LLM Semantic Augmentation for Heterogeneous Treatment Effect Estimation Alaa and Mihaela van der Schaar

Reference 2

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Observation 033bed1f-8aef-4050-9b14-fe95b30cbfe6 · outbound

This paper cites Uncertainty- based offline reinforcement learning with diversified Q-ensemble.

Uncertainty-Guided LLM Semantic Augmentation for Heterogeneous Treatment Effect Estimation Uncertainty- based offline reinforcement learning with diversified Q-ensemble

Reference 3

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Observation 696ef70c-8b71-4367-9178-dd94e4265308 · outbound

This paper cites Pessimistic Bootstrapping for Uncertainty-Driven Offline Reinforcement Learning.

Uncertainty-Guided LLM Semantic Augmentation for Heterogeneous Treatment Effect Estimation Pessimistic Bootstrapping for Uncertainty-Driven Offline Reinforcement Learning

Reference 4

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Observation 59da51b5-1dd0-412f-a304-2089c1c21862 · outbound

This paper cites LLM-driven causal discovery via harmonized prior.IEEE Transactions on Knowledge and Data Engineering, 2025.

Uncertainty-Guided LLM Semantic Augmentation for Heterogeneous Treatment Effect Estimation LLM-driven causal discovery via harmonized prior.IEEE Transactions on Knowledge and Data Engineering, 2025

Reference 5

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Observation aa28ef0c-f200-47c8-aae1-0b9a1f89a589 · outbound

This paper cites Weight uncertainty in neural networks.

Uncertainty-Guided LLM Semantic Augmentation for Heterogeneous Treatment Effect Estimation Weight uncertainty in neural networks

Reference 6

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Observation e6a39eb9-8053-4c7b-9e31-2303ec9d4d0d · outbound

This paper cites Chen, Rohit Bhattacharya, and Katherine A.

Uncertainty-Guided LLM Semantic Augmentation for Heterogeneous Treatment Effect Estimation Chen, Rohit Bhattacharya, and Katherine A

Reference 7

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Observation 421416e0-a85a-4dbf-8509-7061acc0c832 · outbound

This paper cites Unveiling causal reasoning in large language models: Reality or mirage? InAdvances in Neural Information Processing Systems, volume 37, 2024.

Uncertainty-Guided LLM Semantic Augmentation for Heterogeneous Treatment Effect Estimation Unveiling causal reasoning in large language models: Reality or mirage? InAdvances in Neural Information Processing Systems, volume 37, 2024

Reference 8

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Observation 7af29812-fc2a-4859-b1db-71e2e48c45b4 · outbound

This paper cites Krishnan, and Chris J.

Uncertainty-Guided LLM Semantic Augmentation for Heterogeneous Treatment Effect Estimation Krishnan, and Chris J

Reference 9

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Observation c66745b5-5054-4e64-a131-ab68616d79ae · outbound

This paper cites Causal discovery through synergizing large language model and data-driven reasoning.

Uncertainty-Guided LLM Semantic Augmentation for Heterogeneous Treatment Effect Estimation Causal discovery through synergizing large language model and data-driven reasoning

Reference 10

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Observation b42ad967-6c91-4f22-a599-6d1417016525 · outbound

This paper cites Dropout as a bayesian approximation: Representing model uncertainty in deep learning.

Uncertainty-Guided LLM Semantic Augmentation for Heterogeneous Treatment Effect Estimation Dropout as a bayesian approximation: Representing model uncertainty in deep learning

Reference 11

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Observation 6e667001-6419-41b3-a849-2ad1feb2acc5 · outbound

This paper cites Learning disentangled representations for counterfactual regression.

Uncertainty-Guided LLM Semantic Augmentation for Heterogeneous Treatment Effect Estimation Learning disentangled representations for counterfactual regression

Reference 12

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Observation e5699297-290b-4f7c-8593-8b12e8c9d15e · outbound

This paper cites Improving treatment effect estimation with llm-based data augmentation.

Uncertainty-Guided LLM Semantic Augmentation for Heterogeneous Treatment Effect Estimation Improving treatment effect estimation with llm-based data augmentation

Reference 13

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Observation 7baaeb9b-b3ac-46af-aa3e-7baba00b86b2 · outbound

This paper cites Imbens and Donald B.

Uncertainty-Guided LLM Semantic Augmentation for Heterogeneous Treatment Effect Estimation Imbens and Donald B

Reference 14

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Observation 8b6e7a6e-6c0d-4ef2-8f3a-ad182b2f6e22 · outbound

This paper cites Identifying causal- effect inference failure with uncertainty-aware models.Advances in Neural Information Processing Systems, 33:11637–11649, 2020.

Uncertainty-Guided LLM Semantic Augmentation for Heterogeneous Treatment Effect Estimation Identifying causal- effect inference failure with uncertainty-aware models.Advances in Neural Information Processing Systems, 33:11637–11649, 2020

Reference 15

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Observation 42e39038-9c4f-4e96-aed5-b1b3df6db535 · outbound

This paper cites Cladder: Assessing causal reasoning in language models.

Uncertainty-Guided LLM Semantic Augmentation for Heterogeneous Treatment Effect Estimation Cladder: Assessing causal reasoning in language models

Reference 16

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Observation 037a8b5f-949d-432a-ba60-f95435999ead · outbound

This paper cites What uncertainties do we need in bayesian deep learning for computer vision?Advances in neural information processing systems, 30, 2017.

Uncertainty-Guided LLM Semantic Augmentation for Heterogeneous Treatment Effect Estimation What uncertainties do we need in bayesian deep learning for computer vision?Advances in neural information processing systems, 30, 2017

Reference 17

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Observation 9e936f90-7bc1-4ba9-8f76-ad355e4a5ff8 · outbound

This paper cites Varia- tional autoencoders and nonlinear ica: A unifying framework.

Uncertainty-Guided LLM Semantic Augmentation for Heterogeneous Treatment Effect Estimation Varia- tional autoencoders and nonlinear ica: A unifying framework

Reference 18

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Observation c76a17c4-cc4b-4b9e-81c3-5bcfbc891000 · outbound

This paper cites Metalearners for estimating heterogeneous treatment effects using machine learning.Proceedings of the National Academy of Sciences, 116(10):4156–4165, 2019.

Uncertainty-Guided LLM Semantic Augmentation for Heterogeneous Treatment Effect Estimation Metalearners for estimating heterogeneous treatment effects using machine learning.Proceedings of the National Academy of Sciences, 116(10):4156–4165, 2019

Reference 19

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Observation f2b3b8c6-c877-45ac-8db1-2309f16ccb3f · outbound

This paper cites Causal Reasoning and Large Language Models: Opening a New Frontier for Causality.

Uncertainty-Guided LLM Semantic Augmentation for Heterogeneous Treatment Effect Estimation Causal Reasoning and Large Language Models: Opening a New Frontier for Causality

Reference 20

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Observation 9807c25f-da5a-41dc-9cf4-f985949a1f6a · outbound

This paper cites Simple and scalable predictive uncertainty estimation using deep ensembles.Advances in neural information processing systems, 30, 2017.

Uncertainty-Guided LLM Semantic Augmentation for Heterogeneous Treatment Effect Estimation Simple and scalable predictive uncertainty estimation using deep ensembles.Advances in neural information processing systems, 30, 2017

Reference 21

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Observation a303b7e9-19d6-44db-8241-6ea3f680b085 · outbound

This paper cites Self-distilled disentangled learning for counterfactual prediction.

Uncertainty-Guided LLM Semantic Augmentation for Heterogeneous Treatment Effect Estimation Self-distilled disentangled learning for counterfactual prediction

Reference 22

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Observation 4a152543-3d9d-44dc-8b7a-c4d6cd6011c1 · outbound

This paper cites Large language models and causal inference in collaboration: A comprehensive survey.

Uncertainty-Guided LLM Semantic Augmentation for Heterogeneous Treatment Effect Estimation Large language models and causal inference in collaboration: A comprehensive survey

Reference 23

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Observation 6bbea267-b6ba-4fd5-a8b2-a88eeb80124c · outbound

This paper cites Causal effect inference with deep latent-variable models.

Uncertainty-Guided LLM Semantic Augmentation for Heterogeneous Treatment Effect Estimation Causal effect inference with deep latent-variable models

Reference 24

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Observation 0ff9b957-cbde-4c7c-b5e1-8d1ef0e86e19 · outbound

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Uncertainty-Guided LLM Semantic Augmentation for Heterogeneous Treatment Effect Estimation Unresolved cited work

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Observation 1372329a-128a-475a-b71f-2b215a725999 · outbound

This paper cites Deep Disentangled Representation Network for Treatment Effect Estimation.

Uncertainty-Guided LLM Semantic Augmentation for Heterogeneous Treatment Effect Estimation Deep Disentangled Representation Network for Treatment Effect Estimation

Reference 26

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Observation 84774d33-bb8b-47fa-ae16-f324601f8b5a · outbound

This paper cites Quasi-oracle estimation of heterogeneous treat- ment effects.Biometrika, 108(2):299–319, 2021.

Uncertainty-Guided LLM Semantic Augmentation for Heterogeneous Treatment Effect Estimation Quasi-oracle estimation of heterogeneous treat- ment effects.Biometrika, 108(2):299–319, 2021

Reference 27

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Observation d50d0a30-0e9c-4818-9d4d-00a6f33199ab · outbound

This paper cites A critical look at the consistency of causal estimation with deep latent variable models.

Uncertainty-Guided LLM Semantic Augmentation for Heterogeneous Treatment Effect Estimation A critical look at the consistency of causal estimation with deep latent variable models

Reference 28

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Observation 14088521-4329-4cb1-a3b1-411e87f2c12e · outbound

This paper cites Rosenbaum and Donald B.

Uncertainty-Guided LLM Semantic Augmentation for Heterogeneous Treatment Effect Estimation Rosenbaum and Donald B

Reference 29

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Observation 8d6f3293-8f49-431f-aeb4-cf0385e5adc0 · outbound

This paper cites Estimating individual treat- ment effect: generalization bounds and algorithms.

Uncertainty-Guided LLM Semantic Augmentation for Heterogeneous Treatment Effect Estimation Estimating individual treat- ment effect: generalization bounds and algorithms

Reference 30

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Observation e8c5487b-aa85-4c8d-9d09-eded93896659 · outbound

This paper cites Adapting neural networks for the estimation of treatment effects.

Uncertainty-Guided LLM Semantic Augmentation for Heterogeneous Treatment Effect Estimation Adapting neural networks for the estimation of treatment effects

Reference 31

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Observation a8f2594d-2501-4ea3-a468-222e58b63116 · outbound

This paper cites Improving data-driven heterogeneous treatment effect estimation under structure uncertainty.

Uncertainty-Guided LLM Semantic Augmentation for Heterogeneous Treatment Effect Estimation Improving data-driven heterogeneous treatment effect estimation under structure uncertainty

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Observation b1780ec7-3d26-4d7c-97e0-773adc5607e7 · outbound

This paper cites DoubleLingo: Causal estimation with large language models.

Uncertainty-Guided LLM Semantic Augmentation for Heterogeneous Treatment Effect Estimation DoubleLingo: Causal estimation with large language models

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Observation 32873242-6f16-4ea4-b92b-9876e47ac61e · outbound

This paper cites CE-RCFR: Robust counterfactual regression for consensus-enabled treatment effect estimation.

Uncertainty-Guided LLM Semantic Augmentation for Heterogeneous Treatment Effect Estimation CE-RCFR: Robust counterfactual regression for consensus-enabled treatment effect estimation

Reference 34

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Observation 657e70c8-05ed-4bdd-ba00-eff70bcb121a · outbound

This paper cites Treatment effect estimation with adjustment feature selection.

Uncertainty-Guided LLM Semantic Augmentation for Heterogeneous Treatment Effect Estimation Treatment effect estimation with adjustment feature selection

Reference 35

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Observation 1b45e46b-6951-457e-af85-cb268a4095da · outbound

This paper cites Progressive generalization risk reduction for data-efficient causal effect estimation.

Uncertainty-Guided LLM Semantic Augmentation for Heterogeneous Treatment Effect Estimation Progressive generalization risk reduction for data-efficient causal effect estimation

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Observation 31a6e0e1-34e9-457f-8646-aae3ed073cc3 · outbound

This paper cites From supervised to generative: A novel paradigm for tabular deep learning with large language models.

Uncertainty-Guided LLM Semantic Augmentation for Heterogeneous Treatment Effect Estimation From supervised to generative: A novel paradigm for tabular deep learning with large language models

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Observation 36f25a34-8dc4-431f-a493-13cf868ac332 · outbound

This paper cites Learning decomposed representations for treatment effect estimation.IEEE Transactions on Knowledge and Data Engineering, 35(5):4989– 5001, 2023.

Uncertainty-Guided LLM Semantic Augmentation for Heterogeneous Treatment Effect Estimation Learning decomposed representations for treatment effect estimation.IEEE Transactions on Knowledge and Data Engineering, 35(5):4989– 5001, 2023

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source=pdf_text observed=2026-08-01T12:41:27.447347Z digest=sha256:160ae3422b5e88290e7a161c8a66222a66e729da76cf8d01c75362fe12856559

Observation 64e7831c-8ff5-4f97-b8b3-f42f495ff0b5 · outbound

This paper cites 𝛽-intact-VAE: Identifying and estimat- ing causal effects under limited overlap.

Uncertainty-Guided LLM Semantic Augmentation for Heterogeneous Treatment Effect Estimation 𝛽-intact-VAE: Identifying and estimat- ing causal effects under limited overlap

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source=pdf_text observed=2026-08-01T12:41:27.451448Z digest=sha256:d237e193c530d82ae8fe2b60722d2b69b3c8c86fad41981bf5f3b9c490d9efb7

Observation 9b78a919-2db4-40d0-8f21-70201db89ad9 · outbound

This paper cites Uncertainty Weighted Actor-Critic for Offline Reinforcement Learning.

Uncertainty-Guided LLM Semantic Augmentation for Heterogeneous Treatment Effect Estimation Uncertainty Weighted Actor-Critic for Offline Reinforcement Learning

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source=pdf_text observed=2026-08-01T12:41:27.456257Z digest=sha256:7812feb0fda229a1ccd3f039aa4676bd7d5ea515351d231537bf9d22b7b500d7

Observation 57ac5e0b-63bc-461a-8057-86a828d26d5a · outbound

This paper cites Neural contextual bandits with deep representation and shallow exploration.

Uncertainty-Guided LLM Semantic Augmentation for Heterogeneous Treatment Effect Estimation Neural contextual bandits with deep representation and shallow exploration

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source=pdf_text observed=2026-08-01T12:41:27.460677Z digest=sha256:d4482caeddf94360149ba38ce2ed66831da0a2128e631715b949d47cbb7c12d4

Observation bcfbbc86-fd38-4d95-b54b-5f4135e03d1d · outbound

This paper cites Causal inference with conditional front-door adjustment and identifiable variational autoencoder.

Uncertainty-Guided LLM Semantic Augmentation for Heterogeneous Treatment Effect Estimation Causal inference with conditional front-door adjustment and identifiable variational autoencoder

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source=pdf_text observed=2026-08-01T12:41:27.464939Z digest=sha256:798a587f3acdec24e26e5c39f34067ecadb5434d320e5800588303ea4f88b2f3

Observation 2a218f36-8230-44d4-b766-19004853c1fd · outbound

This paper cites Treatment effect estimation with dis- entangled latent factors.Proceedings of the AAAI Conference on Artificial Intel- ligence, 35(12):10923–10930, May 2021.

Uncertainty-Guided LLM Semantic Augmentation for Heterogeneous Treatment Effect Estimation 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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doi, observed 2026-08-01T12:43:31.714403Z

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No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-08-01T12:41:27.469136Z digest=sha256:80440b3793a62f1cf814313ed93d2fe0d813d84cd67f69566e68783c41a6b95a

Observation 1de501b9-9fd6-47e6-923a-b1c66043cf8a · outbound

This paper cites Lui, and Hang Li.

Uncertainty-Guided LLM Semantic Augmentation for Heterogeneous Treatment Effect Estimation Lui, and Hang Li

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source=pdf_text observed=2026-08-01T12:41:27.473229Z digest=sha256:c50b365ebca20a1c28d115bcf589d15c8e6a24a019b6b8709567a23e7e092a7d

Observation 5aeec8cc-b64f-441f-8b69-8a3f5d623be8 · outbound

This paper cites DESCN: Deep entire space cross networks for individual treatment effect estimation.

Uncertainty-Guided LLM Semantic Augmentation for Heterogeneous Treatment Effect Estimation DESCN: Deep entire space cross networks for individual treatment effect estimation

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source=pdf_text observed=2026-08-01T12:41:27.477715Z digest=sha256:5c054770784a9f5e8aba188e458a842ecc0eb5055ebfa0fb9f9cda1629cff801

Observation d8097e7b-4c6c-4418-b3f7-982a53882d02 · outbound

This paper cites Neural contextual bandits with UCB-based exploration.

Uncertainty-Guided LLM Semantic Augmentation for Heterogeneous Treatment Effect Estimation Neural contextual bandits with UCB-based exploration

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source=pdf_text observed=2026-08-01T12:41:27.481763Z digest=sha256:bd15842a620839b50bdb3f48ecdee8ff65c7d8a45e7feb4eb65d2c9397b99df1

Observation ec8f0562-e156-4ce7-98ca-a297518ab3b1 · outbound

This paper cites Causal effect estimation with mixed latent confounders and post-treatment variables.

Uncertainty-Guided LLM Semantic Augmentation for Heterogeneous Treatment Effect Estimation Causal effect estimation with mixed latent confounders and post-treatment variables

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source=pdf_text observed=2026-08-01T12:41:27.485592Z digest=sha256:b9db94d6641467b3da8ed5805f03b39604f5023a3f7d9003a056255eb0992c30

Observation d4bdfc35-4679-4e12-95a2-e636be5a9ab5 · outbound

This paper cites an unresolved cited work.

Uncertainty-Guided LLM Semantic Augmentation for Heterogeneous Treatment Effect Estimation Unresolved cited work

Reference 50

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source=pdf_text observed=2026-08-01T12:41:27.489936Z digest=sha256:df927cc227d2d191d1f143eff394e655a257aff14e6800c0cdb514a301f882ad

Observation 78923274-5844-4157-9c13-457ef804c74e · outbound

This paper cites an unresolved cited work.

Uncertainty-Guided LLM Semantic Augmentation for Heterogeneous Treatment Effect Estimation Unresolved cited work

Reference 51

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source=pdf_text observed=2026-08-01T12:41:27.494066Z digest=sha256:26d873e55b25af8b8d56fcd1ab6ff3ce881f23048789a6818b24df156db00f60

Observation 5ba2c5f0-68e3-4418-b188-3584984cf275 · outbound

This paper cites [Goal] Compress these pre-existing confounders into a single representation.

Uncertainty-Guided LLM Semantic Augmentation for Heterogeneous Treatment Effect Estimation [Goal] Compress these pre-existing confounders into a single representation

Reference 52

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source=pdf_text observed=2026-08-01T12:41:27.498558Z digest=sha256:470f739a8dc8d2b38d80f2e27f652217f94db2ba9d64490cfd8f75d7763f16f9

Observation eb15fe58-b304-45a1-8877-2010fb63079d · outbound

This paper cites 12 Uncertainty-Guided LLM Semantic Augmentation for Heterogeneous Treatment Effect Estimation.

Uncertainty-Guided LLM Semantic Augmentation for Heterogeneous Treatment Effect Estimation 12 Uncertainty-Guided LLM Semantic Augmentation for Heterogeneous Treatment Effect Estimation

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source=pdf_text observed=2026-08-01T12:41:27.503187Z digest=sha256:63b7e347e310ef2f29732750c5ed19ec9e06bbafa9ae1a086a0b7ed11639ff8c

Observation 5ed14ebd-0f44-4968-89fa-0226a686f849 · outbound

This paper cites an unresolved cited work.

Uncertainty-Guided LLM Semantic Augmentation for Heterogeneous Treatment Effect Estimation Unresolved cited work

Reference 54

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source=pdf_text observed=2026-08-01T12:41:27.507280Z digest=sha256:e4f1de9c949a5b5206790083b33e8d8a97703fb03bf3e3c7b90b5188a4948024

Observation d291dbb2-6163-4d7f-8cd1-f7858f0c3c1e · outbound

This paper cites [Goal] Compress these heterogeneity modifiers into a single representation.

Uncertainty-Guided LLM Semantic Augmentation for Heterogeneous Treatment Effect Estimation [Goal] Compress these heterogeneity modifiers into a single representation

Reference 55

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source=pdf_text observed=2026-08-01T12:41:27.511381Z digest=sha256:ce1eefda1aaea893986515c7d2e34ceca43bca9ae2ed38ce68733579db649e68

Observation 392b7d7f-ef7c-499d-b184-ca4a862b1235 · outbound

This paper cites an unresolved cited work.

Uncertainty-Guided LLM Semantic Augmentation for Heterogeneous Treatment Effect Estimation Unresolved cited work

Reference 56

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source=pdf_text observed=2026-08-01T12:41:27.515445Z digest=sha256:51d3c1603516fd25036e2ce236846dc9cc89615eefcccd62f464bfc304e0aa86

Observation ef38c35e-aea3-42c7-8b27-d15875faafd1 · outbound

This paper cites [Goal] Compress these heterogeneity modifiers into a single representation.

Uncertainty-Guided LLM Semantic Augmentation for Heterogeneous Treatment Effect Estimation [Goal] Compress these heterogeneity modifiers into a single representation

Reference 57

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source=pdf_text observed=2026-08-01T12:41:27.525309Z digest=sha256:53fc1ee7b925b0e546c58946ea4cf062ebfcf1ff6893a9a655d1681a778091ba

Observation a5d300f9-e55e-4f31-b7cf-c294b301f8a2 · outbound

This paper cites This suggests a certain socio-economic standing but does not provide detailed information about income, education, or employment status.

Uncertainty-Guided LLM Semantic Augmentation for Heterogeneous Treatment Effect Estimation This suggests a certain socio-economic standing but does not provide detailed information about income, education, or employment status

Reference 58

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source=pdf_text observed=2026-08-01T12:41:27.533928Z digest=sha256:fb2dec1bdf4782290cc946c9a33d1f0edacdca7433ae935e49d26ff025d493c0

Observation 5354d751-460e-4da9-a92e-9f71013745fd · outbound

This paper cites However, as a new customer, they may still be relatively sensitive to new brand strategies or product changes.

Uncertainty-Guided LLM Semantic Augmentation for Heterogeneous Treatment Effect Estimation However, as a new customer, they may still be relatively sensitive to new brand strategies or product changes

Reference 59

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source=pdf_text observed=2026-08-01T12:41:27.538063Z digest=sha256:a994c02df59134f1225666069caf41e58e7ad94ec66c42031f2c133aa61b3603

Observation 35deceb8-a5ee-4570-a060-7522c4a430e6 · outbound

This paper cites doi: 10.1145/3534678.3539444.

Uncertainty-Guided LLM Semantic Augmentation for Heterogeneous Treatment Effect Estimation doi: 10.1145/3534678.3539444

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source=pdf_text observed=2026-08-01T12:41:27.419136Z digest=sha256:e57cc6a96bba1e2bca4ab5295dd567d3799855ed285fa452e219671658352dd6

Observation 04293d30-442b-450c-a8e3-45b993d10432 · outbound

This paper cites an unresolved cited work.

Uncertainty-Guided LLM Semantic Augmentation for Heterogeneous Treatment Effect Estimation Unresolved cited work

Reference 2024

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source=pdf_text observed=2026-08-01T12:41:27.443235Z digest=sha256:c71cca4595ed938954916cc1068d7e4895f891e405d7fc52050f172adcc94b3b

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