Research

Statistical foundations for reliable decisions.

My research spans experimentation, causal inference, and AI evaluation, with a focus on the validity and practical use of statistical methods.

Experimentation and causal inference

Experimental design, variance reduction, rerandomization, interference, and dependent observations.

Statistical methods for evaluation

Complex metrics, sampling design, and uncertainty estimation for reliable decisions.

AI and LLM evaluation

Sampling and inference for LLM evaluation, evaluator reliability, and the relationship between offline model comparisons and online product effects.

Selected publications

COSTA: Covariance-Optimized Design and Causal Inference under Network-Temporal Interference. Qianyi Chen, Bo Li, Yongli Qin, Jinyong Ma. arXiv preprint, 2026. https://arxiv.org/abs/2609.02032v1

Ensuring Trustworthy Online A/B Testing: Addressing Five Key Questions on CUPED. Yu Zhang, Bokui Wan, Yongli Qin, Jinyong Ma, Yifan Guo. arXiv preprint, 2026. https://arxiv.org/abs/2606.18750v1

Bridging Control Variates and Regression Adjustment in A/B Testing: From Design-Based to Model-Based Frameworks. Yu Zhang, Bokui Wan, Yongli Qin. arXiv preprint, 2025. https://arxiv.org/abs/2509.13944v2

Combining Experimental and Historical Data for Policy Evaluation. Ting Li, Chengchun Shi, Qianglin Wen, Yang Sui, Yongli Qin, Chunbo Lai, Hongtu Zhu. arXiv preprint, 2024. https://arxiv.org/abs/2406.00317v1

Full publication list: https://scholar.google.com/citations?user=RgDFUF8AAAAJ&hl=en