1 citations · 1 across the 12 of their papers we have counts for
14 papers
GSP Autobidding Equilibria with Few Advertisers or Slots
Yiyang Huang, Zhengyang Liu, Yonglei Yan +1
In sponsored-shopping auctions, one advertiser may win several positions, and a product that is never displayed may determine the prices of several winners. Under generalized secon…
Multinomial Subset Routing with Sum-Max Rewards and Operational Constraints
Quan Zhou, Yiyan Huang
We study online routing to subsets of experts under aggregate bandit feedback and long-run operational constraints. Expert contributions can be complementary: for each task dimensi…
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…
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…
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…
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…
The Causal Impact of Credit Lines on Spending Distributions
Yijun Li, Cheuk Hang Leung, Xiangqian Sun +6
Consumer credit services offered by e-commerce platforms provide customers with convenient loan access during shopping and have the potential to stimulate sales. To understand the…
Deep into The Domain Shift: Transfer Learning through Dependence Regularization
Shumin Ma, Zhiri Yuan, Qi Wu +5
Classical Domain Adaptation methods acquire transferability by regularizing the overall distributional discrepancies between features in the source domain (labeled) and features in…
Moderately-Balanced Representation Learning for Treatment Effects with Orthogonality Information
Yiyan Huang, Cheuk Hang Leung, Shumin Ma +3
Estimating the average treatment effect (ATE) from observational data is challenging due to selection bias. Existing works mainly tackle this challenge in two ways. Some researcher…
Robust Causal Learning for the Estimation of Average Treatment Effects
Yiyan Huang, Cheuk Hang Leung, Xing Yan +5
Many practical decision-making problems in economics and healthcare seek to estimate the average treatment effect (ATE) from observational data. The Double/Debiased Machine Learnin…
Robust Orthogonal Machine Learning of Treatment Effects
Yiyan Huang, Cheuk Hang Leung, Qi Wu +1
Causal learning is the key to obtaining stable predictions and answering \textit{what if} problems in decision-makings. In causal learning, it is central to seek methods to estimat…
The Causal Learning of Retail Delinquency
Yiyan Huang, Cheuk Hang Leung, Xing Yan +4
This paper focuses on the expected difference in borrower's repayment when there is a change in the lender's credit decisions. Classical estimators overlook the confounding effects…