7 papers
Toward an Asymptotic Efficiency Theory on Regular Parameter Manifolds
Lvfang Sun, Zhenhua Lin, Lin Liu
Asymptotic efficiency theory is one of the pillars in the foundations of modern mathematical statistics. Not only does it serve as a rigorous theoretical benchmark for evaluating s…
Bias reduction in g-computation for covariate adjustment in randomized clinical trials
Xin Zhang, Lin Liu, Haitao Chu
G-computation is a powerful method for estimating unconditional treatment effects with covariate adjustment in randomized clinical trials. It typically relies on fitting canonical…
Few-shot Multi-Task Learning of Linear Invariant Features with Meta Subspace Pursuit
Chaozhi Zhang, Lin Liu, Xiaoqun Zhang
Data scarcity poses a serious threat to modern machine learning and artificial intelligence, as their practical success typically relies on the availability of big datasets. One ef…
Assumption-lean covariate adjustment under covariate adaptive randomization when
Yujia Gu, Lin Liu, Wei Ma
Adjusting for (baseline) covariates with working regression models becomes standard practice in the analysis of randomized clinical trials (RCT). When the dimension of the cova…
Covariate Adjustment in Randomized Experiments Motivated by Higher-Order Influence Functions
Sihui Zhao, Xinbo Wang, Lin Liu +1
Higher-Order Influence Functions (HOIF), developed in a series of papers over the past twenty years, are a fundamental theoretical device for constructing rate-optimal causal-effec…
Marginal Causal Effect Estimation with Continuous Instrumental Variables
Mei Dong, Lin Liu, Dingke Tang +3
Instrumental variables (IVs) are often continuous, arising in diverse fields such as economics, epidemiology, and the social sciences. Existing approaches for continuous IVs typica…