arXiv Machine Learning June 17, 2026 By Yuxuan Yang, Dugang Liu, Yiyan Huang

Evaluating Uplift Modeling under Structural Biases: Insights into Metric Stability and Model Robustness

Read the original on arXiv Machine Learning →

arXiv:2603. 20775v2 Announce Type: replace Abstract: In personalized marketing, uplift models estimate the incremental effect of an intervention by modeling how customer behavior would change under alternative treatments using counterfactual analysis.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv Machine Learning

Jun 2

Jointly Optimizing Debiased CTR and Uplift for Coupons Marketing: A Unified Causal Framework

arXiv:2602. 12972v2 Announce Type: replace-cross Abstract: In online advertising, marketing interventions such as coupons introduce significant confounding bias into Click-Through Rate (CTR) prediction.

By Siyun Yang, Shixiao Yang, Jian Wang, Di Fan, Kehe Cai, Haoyan Fu, Jiaming Zhang, Wenjin Wu, Peng Jiang

safety

arXiv Machine Learning

Jul 2

From Structural Equation Modelling to Double Machine Learning: Robustness Analysis for Survey-Based Research

arXiv:2607. 00512v1 Announce Type: new Abstract: Structural equation modelling (SEM) is widely used in survey-based business and information systems research to assess latent constructs and theory-driven structural relationships.

By Ka Ching Chan, Qiana Liu, Sanjib Tiwari, Ranga Chimhundu

benchmarks

arXiv Machine Learning

Aug 4

UpliftBench: Revealing Outcome-Regime and Objective Mismatch in Uplift Evaluation

arXiv:2608. 00915v1 Announce Type: new Abstract: Uplift modeling (conditional-average-treatment-effect estimation) drives personalized targeting, yet published uplift benchmarks frequently disagree on which estimator performs best; we show the disagreement is substantially about metrics, not models.

By Binshuang Li

benchmarks safety

arXiv Machine Learning

Jul 22

How Benchmark Prediction from Fewer Data Misses the Mark

arXiv:2506. 07673v2 Announce Type: replace Abstract: Large language model (LLM) evaluation is increasingly costly, prompting interest in methods that speed up evaluation by shrinking benchmark datasets.

By Guanhua Zhang, Florian E. Dorner, Moritz Hardt

llms benchmarks

arXiv AI

Jun 30

HERO: Improving the Reliability and Sensitivity of Generative Model Evaluation Using Historical Data

arXiv:2606. 29784v1 Announce Type: cross Abstract: Reliable generative AI models critically rely on expert human annotations to evaluate output quality, yet these "gold" labels are expensive to collect and limited in quantity.

By Xinrui Ruan, Zhenyu Zhao, Waverly Wei, Yueshan Zhang, Zeyu Zheng, Sui Huang, Jingshen Wang

benchmarks safety

arXiv AI

Jun 11

A Survey on Evaluating Quality and Trustworthiness in LLM-Generated Data

arXiv:2601. 17717v3 Announce Type: replace Abstract: Large Language Models (LLMs) have emerged as powerful tools for generating data across various modalities.

By Kaituo Zhang, Mingzhi Hu, Hoang Anh Duy Le, Fariha Kabir Torsha, Zhimeng Jiang, Minh Khai Bui, Chia-Yuan Chang, Yu-Neng Chuang, Zhen Xiong, Ying Lin, Guanchu Wang, Na Zou

llms

Related stories

Jointly Optimizing Debiased CTR and Uplift for Coupons Marketing: A Unified Causal Framework

From Structural Equation Modelling to Double Machine Learning: Robustness Analysis for Survey-Based Research

UpliftBench: Revealing Outcome-Regime and Objective Mismatch in Uplift Evaluation

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HERO: Improving the Reliability and Sensitivity of Generative Model Evaluation Using Historical Data

A Survey on Evaluating Quality and Trustworthiness in LLM-Generated Data