4 papers
Data-Automated Policy Learning for Nonlinear Welfare
Chunrong Ai, Zeqi Wu, Zheng Zhang
This paper explores policy learning from observational data, focusing on a nonlinear welfare criterion in a binary treatment setting. The nonlinear criterion is inspired by scenari…
Limit Theorems for Network Data without Metric Structure
Wen Jiang, Yachen Wang, Zeqi Wu +1
This paper develops limit theorems for random variables with network dependence, without requiring the individuals in the network to be located in a Euclidean or metric space. This…
Integrating Heterogeneous Information in Randomized Experiments: A Unified Calibration Framework
Wei Ma, Zeqi Wu, Zheng Zhang
In modern randomized experiments, large-scale data collection increasingly yields rich baseline covariates and auxiliary information from multiple sources. Such information offers…
A New and Efficient Debiased Estimation of General Treatment Models by Balanced Neural Networks Weighting
Zeqi Wu, Meilin Wang, Wei Huang +1
Estimation and inference of treatment effects under unconfounded treatment assignments often suffer from bias and the `curse of dimensionality' due to the nonparametric estimation…