In this paper, we review recent advances in statistical methods for the evaluation of the
heterogeneity of treatment effects (HTE), including subgroup identification and estimation of …

Quantifying treatment effect heterogeneity is a crucial task in many areas of causal
inference, eg optimal treatment allocation and estimation of subgroup effects. We study the …

Optimal nonparametric testing of Missing Completely At Random and its connections to
compatibility Page 1 The Annals of Statistics 2023, Vol. 51, No. 5, 2170–2193 https://doi.org/10.1214/23-AOS2326 …

These lecture notes provide an overview of existing methodologies and recent
developments for estimation and inference with high dimensional time series regression …

When multiple candidate subgroups are considered in clinical trials, we often need to make
statistical inference on the subgroups simultaneously. Classical multiple testing procedures …

Given a sample of covariate-response pairs, we consider the subgroup selection problem of
identifying a subset of the covariate domain where the regression function exceeds a pre …

Subgroup analysis may be used to investigate treatment effect heterogeneity among subsets
of the study population defined by baseline characteristics. Several methodologies have …

Regression modeling is the workhorse of statistics and there is a vast literature on estimation
of the regression function. It has been realized in recent years that in regression analysis the …

Variable selection, monotone likelihood ratio and group sparsity Page 1 The Annals of
Statistics 2023, Vol. 51, No. 1, 312–333 https://doi.org/10.1214/22-AOS2251 © Institute of …

We study several problems related to the identi cation and the e cient estimation of
parameters arising in causal inference. In the rst part of this thesis, we consider the problem …