119 citations · 603 across the 42 of their papers we have counts for
17 papers · 1 filter
Stabilized Direct Learning for Efficient Estimation of Individualized Treatment Rules
Kushal S. Shah, Haoda Fu, Michael R. Kosorok
In recent years, the field of precision medicine has seen many advancements. Significant focus has been placed on creating algorithms to estimate individualized treatment rules (IT…
A unified nonparametric fiducial approach to interval-censored data
Yifan Cui, Jan Hannig, Michael Kosorok
Censored data, where the event time is partially observed, are challenging for survival probability estimation. In this paper, we introduce a novel nonparametric fiducial approach…
Multi-stage optimal dynamic treatment regimes for survival outcomes with dependent censoring
Hunyong Cho, Shannon T. Holloway, David J. Couper +1
We propose a reinforcement learning method for estimating an optimal dynamic treatment regime for survival outcomes with dependent censoring. The estimator allows the failure time…
Kernel Assisted Learning for Personalized Dose Finding
Liangyu Zhu, Wenbin Lu, Michael R. Kosorok +1
An individualized dose rule recommends a dose level within a continuous safe dose range based on patient level information such as physical conditions, genetic factors and medicati…
Estimating heterogeneous treatment effects with right-censored data via causal survival forests
Yifan Cui, Michael R. Kosorok, Erik Sverdrup +2
Forest-based methods have recently gained in popularity for non-parametric treatment effect estimation. Building on this line of work, we introduce causal survival forests, which c…
Interval censored recursive forests
Hunyong Cho, Nicholas P. Jewell, Michael R. Kosorok
We propose the interval censored recursive forests (ICRF) which is an iterative tree ensemble method for interval censored survival data. This nonparametric regression estimator ma…