Research

Moment Restrictions for Dyadic Network Formation Models with Nontransferable Utility

with Hanping Chen (Sep 2026)

arXiv

Data-Automated Policy Learning for Nonlinear Welfare

with Chunrong Ai and Zheng Zhang (Jun 2026)

arXiv

Limit Theorems for Network Data without Metric Structure

with Wen Jiang, Yachen Wang, and Xingbai Xu (Mar 2026)

arXiv

Integrating Heterogeneous Information in Randomized Experiments: A Unified Calibration Framework

with Wei Ma and Zheng Zhang (Mar 2026)

In modern randomized experiments, large-scale data collection increasingly yields rich baseline covariates and auxiliary information from multiple sources. Such information offers opportunities for more precise treatment effect estimation, but it also raises the challenge of integrating heterogeneous information coherently without compromising validity. Covariate-adaptive randomization (CAR) is widely used to improve covariate balance at the design stage, but it typically balances only a small set of covariates used to form strata, making covariate adjustment at the analysis stage essential for more efficient estimation of treatment effects. Beyond standard covariate adjustment, it is often desirable to incorporate auxiliary information, including cross-stratum information, predictions from various machine learning models, and external data from historical trials or real-world sources. While this auxiliary information is widely available, existing covariate adjustment methods under CAR primarily exploit within-stratum covariates and do not provide a coherent mechanism for integrating it. We propose a unified calibration framework that integrates such information through an information proxy vector and calibration weights defined by a convex optimization problem. The resulting estimator recovers many recent covariate adjustment procedures as special cases while providing a systematic mechanism for both internal and external information borrowing within a single framework. We establish large-sample validity and a no-harm efficiency guarantee, showing that incorporating additional information sources cannot increase asymptotic variance, and we extend the theory to settings in which both the number of strata and the number of information sources grow with the sample size. Simulation studies and an empirical analysis of a field experiment on savings behavior in Uganda and Malawi demonstrate the strong finite-sample performance and practical utility of our method.

arXiv Code

A New and Efficient Debiased Estimation of General Treatment Models by Balanced Neural Networks Weighting

with Wei Huang, Meilin Wang, and Zheng Zhang (Jun 2025)

Estimation and inference of treatment effects under unconfounded treatment assignments often suffer from bias and the “curse of dimensionality” due to the nonparametric estimation of nuisance parameters for high-dimensional confounders. Although debiased state-of-the-art methods have been proposed for binary treatments under particular treatment models, they can be unstable for small sample sizes. Moreover, directly extending them to general treatment models can lead to computational complexity. We propose a balanced neural networks weighting method for general treatment models, which leverages deep neural networks to alleviate the curse of dimensionality while retaining optimal covariate balance through calibration, thereby achieving debiased and robust estimation. Our method accommodates a wide range of treatment models, including average, quantile, distributional, and asymmetric least squares treatment effects, for discrete, continuous, and mixed treatments. Under regularity conditions, we show that our estimator achieves rate double robustness and \(\sqrt{N}\)-asymptotic normality, and its asymptotic variance achieves the semiparametric efficiency bound. We further develop a statistical inference procedure based on weighted bootstrap, which avoids estimating the efficient influence/score functions. Simulation results reveal that the proposed method consistently outperforms existing alternatives, especially when the sample size is small. Applications to the 401(k) dataset and the Mother's Significant Features dataset further illustrate the practical value of the method for estimating both average and quantile treatment effects under binary and continuous treatments, respectively.

arXiv Slides

Peer-reviewed Publications

Applications of Functional Dependence to Spatial Econometrics

Working Papers

Moment Restrictions for Dyadic Network Formation Models with Nontransferable Utility

Data-Automated Policy Learning for Nonlinear Welfare

Limit Theorems for Network Data without Metric Structure

Integrating Heterogeneous Information in Randomized Experiments: A Unified Calibration Framework

A New and Efficient Debiased Estimation of General Treatment Models by Balanced Neural Networks Weighting

Peer-reviewed Publications

Applications of Functional Dependence to Spatial Econometrics ↗

Working Papers

Moment Restrictions for Dyadic Network Formation Models with Nontransferable Utility ↗

Data-Automated Policy Learning for Nonlinear Welfare ↗

Limit Theorems for Network Data without Metric Structure ↗

Integrating Heterogeneous Information in Randomized Experiments: A Unified Calibration Framework ↗

A New and Efficient Debiased Estimation of General Treatment Models by Balanced Neural Networks Weighting ↗

Applications of Functional Dependence to Spatial Econometrics

Moment Restrictions for Dyadic Network Formation Models with Nontransferable Utility

Data-Automated Policy Learning for Nonlinear Welfare

Limit Theorems for Network Data without Metric Structure

Integrating Heterogeneous Information in Randomized Experiments: A Unified Calibration Framework

A New and Efficient Debiased Estimation of General Treatment Models by Balanced Neural Networks Weighting