6 papers
GAUGER: Generalized Regression Adjustment via Graph-Weighted Exposure-Level Residualization for Design-Based Inference Under Interference
Lei Shi, Rita Lyu, Sizhu Lu +1
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…
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…
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…
Conditional cross-fitting for unbiased machine-learning-assisted covariate adjustment in randomized experiments
Xin Lu, Lei Shi, Hanzhong Liu +1
Randomized experiments are the gold standard for estimating the average treatment effect (ATE). While covariate adjustment can reduce the asymptotic variances of the unbiased Horvi…
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…
Asymptotic theory of the quadratic assignment procedure for dyadic data analysis
Lei Shi, Peng Ding
The quadratic assignment procedure (QAP) is a popular tool for analyzing dyadic data in medical and social sciences. To test the association between two dyadic measurements represe…