Emulate randomized clinical trials using heterogeneous treatment effect estimation for personalized treatments: Methodology review and benchmark
Big data and (deep) machine learning have been ambitious tools in digital medicine, but
these tools focus mainly on association. Intervention in medicine is about the causal effects …
these tools focus mainly on association. Intervention in medicine is about the causal effects …
The projected covariance measure for assumption-lean variable significance testing
The projected covariance measure for assumption-lean variable significance testing Page 1
The Annals of Statistics 2024, Vol. 52, No. 6, 2851–2878 https://doi.org/10.1214/24-AOS2447 …
The Annals of Statistics 2024, Vol. 52, No. 6, 2851–2878 https://doi.org/10.1214/24-AOS2447 …
Rank-transformed subsampling: inference for multiple data splitting and exchangeable p-values
Many testing problems are readily amenable to randomized tests, such as those employing
data splitting. However, despite their usefulness in principle, randomized tests have obvious …
data splitting. However, despite their usefulness in principle, randomized tests have obvious …
A new central limit theorem for the augmented ipw estimator: Variance inflation, cross-fit covariance and beyond
Estimation of the average treatment effect (ATE) is a central problem in causal inference. In
recent times, inference for the ATE in the presence of high-dimensional covariates has been …
recent times, inference for the ATE in the presence of high-dimensional covariates has been …
When is the estimated propensity score better? High-dimensional analysis and bias correction
Anecdotally, using an estimated propensity score is superior to the true propensity score in
estimating the average treatment effect based on observational data. However, this claim …
estimating the average treatment effect based on observational data. However, this claim …
Minimax semiparametric learning with approximate sparsity
This paper is about the feasibility and means of root-n consistently estimating linear, mean-
square continuous functionals of a high dimensional, approximately sparse regression …
square continuous functionals of a high dimensional, approximately sparse regression …
Efficient and robust semi-supervised estimation of ATE with partially annotated treatment and response
J Hou, R Mukherjee, T Cai - arXiv preprint arXiv:2110.12336, 2021 - arxiv.org
A notable challenge of leveraging Electronic Health Records (EHR) for treatment effect
assessment is the lack of precise information on important clinical variables, including the …
assessment is the lack of precise information on important clinical variables, including the …
Model-assisted inference for covariate-specific treatment effects with high-dimensional data
Covariate-specific treatment effects (CSTEs) represent heterogeneous treatment effects
across subpopulations defined by certain selected covariates. In this article, we consider …
across subpopulations defined by certain selected covariates. In this article, we consider …
A decorrelation method for general regression adjustment in randomized experiments
We study regression adjustment with general function class approximations for estimating
the average treatment effect in the design-based setting. Standard regression adjustment …
the average treatment effect in the design-based setting. Standard regression adjustment …
Root-n consistent semiparametric learning with high-dimensional nuisance functions under minimal sparsity
Treatment effect estimation under unconfoundedness is a fundamental task in causal
inference. In response to the challenge of analyzing high-dimensional datasets collected in …
inference. In response to the challenge of analyzing high-dimensional datasets collected in …