Modern approaches for evaluating treatment effect heterogeneity from clinical trials and observational data
I Lipkovich, D Svensson, B Ratitch… - Statistics in …, 2024 - Wiley Online Library
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 …
heterogeneity of treatment effects (HTE), including subgroup identification and estimation of …
Learning optimal group-structured individualized treatment rules with many treatments
Data driven individualized decision making problems have received a lot of attentions in
recent years. In particular, decision makers aim to determine the optimal Individualized …
recent years. In particular, decision makers aim to determine the optimal Individualized …
Learning optimal distributionally robust individualized treatment rules
W Mo, Z Qi, Y Liu - Journal of the American Statistical Association, 2021 - Taylor & Francis
Recent development in the data-driven decision science has seen great advances in
individualized decision making. Given data with individual covariates, treatment …
individualized decision making. Given data with individual covariates, treatment …
A semiparametric instrumental variable approach to optimal treatment regimes under endogeneity
Y Cui, E Tchetgen Tchetgen - Journal of the American Statistical …, 2021 - Taylor & Francis
There is a fast-growing literature on estimating optimal treatment regimes based on
randomized trials or observational studies under a key identifying condition of no …
randomized trials or observational studies under a key identifying condition of no …
Learning individualized treatment rules with many treatments: A supervised clustering approach using adaptive fusion
Abstract Learning an optimal Individualized Treatment Rule (ITR) is a very important
problem in precision medicine. This paper is concerned with the challenge when the …
problem in precision medicine. This paper is concerned with the challenge when the …
Optimizing pessimism in dynamic treatment regimes: A bayesian learning approach
In this article, we propose a novel pessimism-based Bayesian learning method for optimal
dynamic treatment regimes in the offline setting. When the coverage condition does not hold …
dynamic treatment regimes in the offline setting. When the coverage condition does not hold …
Rate-optimal contextual online matching bandit
Two-sided online matching platforms have been employed in various markets. However,
agents' preferences in present market are usually implicit and unknown and must be learned …
agents' preferences in present market are usually implicit and unknown and must be learned …
Off-policy evaluation in doubly inhomogeneous environments
Z Bian, C Shi, Z Qi, L Wang - Journal of the American Statistical …, 2024 - Taylor & Francis
This work aims to study off-policy evaluation (OPE) under scenarios where two key
reinforcement learning (RL) assumptions—temporal stationarity and individual homogeneity …
reinforcement learning (RL) assumptions—temporal stationarity and individual homogeneity …
Estimation and validation of ratio-based conditional average treatment effects using observational data
S Yadlowsky, F Pellegrini, F Lionetto… - Journal of the …, 2021 - Taylor & Francis
While sample sizes in randomized clinical trials are large enough to estimate the average
treatment effect well, they are often insufficient for estimation of treatment-covariate …
treatment effect well, they are often insufficient for estimation of treatment-covariate …
Efficient learning of optimal individualized treatment rules for heteroscedastic or misspecified treatment-free effect models
W Mo, Y Liu - Journal of the Royal Statistical Society Series B …, 2022 - academic.oup.com
Recent development in data-driven decision science has seen great advances in
individualized decision making. Given data with individual covariates, treatment …
individualized decision making. Given data with individual covariates, treatment …