4 papers
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…
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…
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…