3 citations · 4 across the 7 of their papers we have counts for
8 papers
TaQFormer: A Treatment-as-Query Transformer with Data-Fusion for Multidimensional Treatment Modeling in Coupon Allocation
Jiahong Li, Zeqin Yang, Yu Xiang +8
We study coupon allocation on an online marketplace offering multiple service categories. Issuing coupons requires choosing a vector of category-specific discounts, accounting for…
Budgeted Active Experimentation for Treatment Effect Estimation from Observational and Randomized Data
Jiacan Gao, Xinyan Su, Mingyuan Ma +7
Estimating heterogeneous treatment effects is central to data-driven decision-making, yet industrial applications often face a fundamental tension between limited randomized contro…
Orthogonal Uplift Learning with Permutation-Invariant Representations for Combinatorial Treatments
Xinyan Su, Jiacan Gao, Mingyuan Ma +6
We study uplift estimation for combinatorial treatments. Uplift measures the pure incremental causal effect of an intervention (e.g., sending a coupon or a marketing message) on us…
The Estimation of Continual Causal Effect for Dataset Shifting Streams
Baining Chen, Yiming Zhang, Yuqiao Han +6
Causal effect estimation has been widely used in marketing optimization. The framework of an uplift model followed by a constrained optimization algorithm is popular in practice. T…
Estimating Long-term Heterogeneous Dose-response Curve: Generalization Bound Leveraging Optimal Transport Weights
Zeqin Yang, Weilin Chen, Ruichu Cai +7
Long-term treatment effect estimation is a significant but challenging problem in many applications. Existing methods rely on ideal assumptions, such as no unobserved confounders o…
Contrastive Balancing Representation Learning for Heterogeneous Dose-Response Curves Estimation
Minqin Zhu, Anpeng Wu, Haoxuan Li +8
Estimating the individuals' potential response to varying treatment doses is crucial for decision-making in areas such as precision medicine and management science. Most recent stu…
Long-term Causal Effects Estimation via Latent Surrogates Representation Learning
Ruichu Cai, Weilin Chen, Zeqin Yang +4
Estimating long-term causal effects based on short-term surrogates is a significant but challenging problem in many real-world applications, e.g., marketing and medicine. Despite i…
GCF: Generalized Causal Forest for Heterogeneous Treatment Effect Estimation in Online Marketplace
Shu Wan, Chen Zheng, Zhonggen Sun +4
Uplift modeling is a rapidly growing approach that utilizes causal inference and machine learning methods to directly estimate the heterogeneous treatment effects, which has been w…