6 papers
Wasserstein Policy Learning for Distributional Outcomes
Yiyan Huang, Cheuk Hang Leung, Qi Wu +1
Offline policy learning has received growing attention in causal inference. The primary objective is to learn a policy (individualized treatment rule) as a mapping from covariates…
Evaluating Uplift Modeling under Structural Biases: Insights into Metric Stability and Model Robustness
Yuxuan Yang, Dugang Liu, Yiyan Huang
In personalized marketing, uplift models estimate the incremental effect of an intervention by modeling how customer behavior would change under alternative treatments using counte…
Pacing Equilibria in Second-Price Auctions with Few Goods
Yiyang Huang, Yonglei Yan, Zihe Wang +1
In this paper, we investigate the computation of second-price pacing equilibria (SPPEs), a foundational model in online advertising auctions. We present a polynomial-time algorithm…
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…
Distributionally Robust Policy Evaluation and Learning for Continuous Treatment with Observational Data
Cheuk Hang Leung, Yiyan Huang, Yijun Li +1
Using offline observational data for policy evaluation and learning allows decision-makers to evaluate and learn a policy that connects characteristics and interventions. Most exis…
Unveiling the Potential of Robustness in Selecting Conditional Average Treatment Effect Estimators
Yiyan Huang, Cheuk Hang Leung, Siyi Wang +2
The growing demand for personalized decision-making has led to a surge of interest in estimating the Conditional Average Treatment Effect (CATE). Various types of CATE estimators h…