2 citations · 2 across the 3 of their papers we have counts for
4 papers
Denoised Conformal Alignment for Reliable Selection of Conditional Average Treatment Effect Predictions
Xinyun Lu, Haoang Chi, Zhiheng Zhang
In selective deployment, practitioners act only on a model-chosen subset of individuals based on predicted conditional average treatment effects, but marginal conformal guarantees…
Design-based edge-level causal inference with machine learning assisted covariate adjustment
Haoyang Yu, Yilin Li, Lu Deng +3
We study design-based causal inference for edge-level outcomes in directed networks under dyadic interference. In this setting, outcomes are defined on directed edges and depend on…
Adjusting auxiliary variables under approximate neighborhood interference
Xin Lu, Yuhao Wang, Zhiheng Zhang
Randomized experiments are the gold standard for causal inference. However, traditional assumptions, such as the Stable Unit Treatment Value Assumption (SUTVA), often fail in real-…
Debiased regression adjustment in completely randomized experiments with moderately high-dimensional covariates
Xin Lu, Fan Yang, Yuhao Wang
Completely randomized experiment is the gold standard for causal inference. When the covariate information for each experimental candidate is available, one typical way is to inclu…