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stat.ME2026

GAUGER: Generalized Regression Adjustment via Graph-Weighted Exposure-Level Residualization for Design-Based Inference Under Interference

Lei Shi, Rita Lyu, Sizhu Lu

Estimating causal effects under interference is a common problem in social science and economics. However, it is challenging due to the complex dependency structure induced by netw…

stat.ME2026

Estimating within-cluster and between-cluster spillover effects in randomized saturation designs

Sizhu Lu, Lei Shi, Peng Ding

Randomized saturation designs are two-stage experiments: they first randomly assign treatment probabilities over the clusters and then randomly assign the treatment to the units wi…

stat.ME2025

TERRA: A Transformer-Enabled Recursive R-learner for Longitudinal Heterogeneous Treatment Effect Estimation

Lei Shi, Sizhu Lu, Qiuran Lyu +2

Accurately estimating heterogeneous treatment effects (HTE) in longitudinal settings is essential for personalized decision-making across healthcare, public policy, education, and…

stat.ME2025

Estimating treatment effects with competing intercurrent events in randomized controlled trials

Sizhu Lu, Yanyao Yi, Yongming Qu +3

The analysis of randomized controlled trials is often complicated by intercurrent events (IEs) -- events that occur after treatment initiation and affect either the interpretation…

stat.ME2025

Design-based causal inference in bipartite experiments

Sizhu Lu, Lei Shi, Yue Fang +2

Bipartite experiments arise in various fields, in which the treatments are randomized over one set of units, while the outcomes are measured over another separate set of units. How…