arXiv Machine Learning

Doubly Robust Estimation of Causal Effect on CVR with Targeted Regularization

arXiv:2608. 13461v1 Announce Type: new Abstract: Post-click conversion rate (CVR) is a key metric in various scenarios including e-commerce and advertising, reflecting the efficiency and user experience in the second stage of the conversion process.

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 AI

Jul 23

Generative Augmented Inference of LLM-generated Data for Market Research: Theory and Empirical Evidence

arXiv:2604. 14575v3 Announce Type: replace-cross Abstract: Marketing research often relies on parameters estimated from costly human-generated data, such as conjoint survey responses, purchase decisions, and field experiment outcomes.

By Cheng Lu, Mengxin Wang, Dennis J. Zhang, Heng Zhang

llms benchmarks

arXiv Machine Learning

Aug 12

MARCO: Click-Intent Decomposition for Calibrated Ads Conversion Prediction

arXiv:2608. 10562v1 Announce Type: new Abstract: Not all clicks are equal.

By Shiwen Shen, Xiru Huang, Liang Luo, Jianbo Sun, He Lyu, Zihang Fu, Ivonne Xu, Zhizhuo Li, Zhengyu Zhang, Pei-Ju Sung, Yunmiao Wang, Zixuan Wang, Zhengli Zhao, Qiang Jin, Mike Jermann, Mingda Li, Yang Xiao, Bhavana Challa, Brooke Bian, Yang Li, Ashish Chamoli, Bibek Bhusal, Danning Di, Yuan Jin, Meet Raval, Zhiwen Chen, Boyao Sun, Shuguang Wang, Yunlong He, Yantao Yao, Sagar Chordia, Wenlin Chen, Santanu Kolay, Qin Huang, Ellie Wen

reinforcement-learning safety

arXiv AI

Aug 12

From Prediction to Incrementality: Causal Optimization for Large-Scale Targeting and Recommendation

arXiv:2608. 10182v1 Announce Type: cross Abstract: Large-scale targeting and recommendation systems are typically built around predictive scores fed into heuristic or local allocation.

By Changshuai Wei, John Bencina, Phuc Nguyen, Andre Assuncao Silva T Ribeiro, Benjamin Zelditch

llms rag reinforcement-learning

arXiv Machine Learning

Jun 3

Tailoring Strictly Proper Scoring Rules for Downstream Tasks: An Application to Causal Inference

arXiv:2606. 03332v1 Announce Type: new Abstract: Probabilistic models are typically trained using task-agnostic objectives like log-loss, which can lead to significant errors in downstream estimation.

By Roman Plaud, Alexandre Perez-Lebel, Antoine Saillenfest, Thomas Bonald, Marine Le Morvan, Ga\"el Varoquaux, Matthieu Labeau

benchmarks safety

arXiv Machine Learning

Jul 17

Causal Inference for Sequential Settings under Interference and Latent Confounding

arXiv:2607. 14940v1 Announce Type: new Abstract: We study causal inference under outcome interference for sequential, observational settings.

By Phevos Paschalidis, Constantinos Daskalakis, Devavrat Shah

arXiv Machine Learning

Jul 1

When few labeled target data suffice: a theory of semi-supervised domain adaptation via fine-tuning from multiple adaptive starts

arXiv:2507. 14661v2 Announce Type: replace-cross Abstract: Semi-supervised domain adaptation (SSDA) seeks to achieve accurate predictions in a target domain with limited labeled target data by exploiting abundant source and unlabeled target data.

By Wooseok Ha, Yuansi Chen

fine-tuning

arXiv Machine Learning

Jul 17

Deep-learning Causal Retrieval Optimization for Efficient e-commerce Distribution in Pinterest

arXiv:2607. 14161v1 Announce Type: cross Abstract: Pinterest is where people turn inspiration into action as users browse ideas, then take steps toward realization, often by discovering shoppable content.

By Junpeng Hou, XianXing Zhang, Sai Xiao, Derek Cheng, Darren Reger, Olafur Gudmundsson, Mehdi Ben Ayed, Zhiqing Rao, Huizhong Duan

arXiv Machine Learning

Jun 17

Statistical Learning from Attribution Sets

arXiv:2602. 06276v2 Announce Type: replace Abstract: We address the problem of training conversion prediction models in advertising domains under privacy constraints, where direct links between ad clicks and conversions are unavailable.

By Lorne Applebaum, Robert Busa-Fekete, August Y. Chen, Claudio Gentile, Tomer Koren, Aryan Mokhtari

safety

arXiv AI

Jul 7

MCLMR: A Model-Agnostic Causal Learning Framework for Multi-Behavior Recommendation

arXiv:2603. 25126v2 Announce Type: replace-cross Abstract: Multi-Behavior Recommendation (MBR) leverages multiple user interaction types (e.

By Ranxu Zhang, Junjie Meng, Ying Sun, Ziqi Xu, Bing Yin, Hao Li, Yanyong Zhang, Chao Wang

efficiency safety

arXiv Machine Learning

Jun 3

Estimating Bidirectional Causal Effects with Large Scale Online Kernel Learning

arXiv:2511. 05050v3 Announce Type: replace-cross Abstract: In this study, a scalable online kernel learning framework is proposed for estimating bidirectional causal effects in systems characterized by mutual dependence and heteroskedasticity.

By Masahiro Tanaka

safety

arXiv Machine Learning

Jun 17

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

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.

By Yuxuan Yang, Dugang Liu, Yiyan Huang

benchmarks safety

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