Estimating heterogeneous treatment effects for general responses
Z Gao, T Hastie - arXiv preprint arXiv:2103.04277, 2021 - arxiv.org
Heterogeneous treatment effect models allow us to compare treatments at subgroup and
individual levels, and are of increasing popularity in applications like personalized medicine …
individual levels, and are of increasing popularity in applications like personalized medicine …
Data-driven estimation of heterogeneous treatment effects
Estimating how a treatment affects different individuals, known as heterogeneous treatment
effect estimation, is an important problem in empirical sciences. In the last few years, there …
effect estimation, is an important problem in empirical sciences. In the last few years, there …
Doubly robust direct learning for estimating conditional average treatment effect
H Meng, X Qiao - arXiv preprint arXiv:2004.10108, 2020 - arxiv.org
Inferring the heterogeneous treatment effect is a fundamental problem in the sciences and
commercial applications. In this paper, we focus on estimating Conditional Average …
commercial applications. In this paper, we focus on estimating Conditional Average …
Combining observational and randomized data for estimating heterogeneous treatment effects
Estimating heterogeneous treatment effects is an important problem across many domains.
In order to accurately estimate such treatment effects, one typically relies on data from …
In order to accurately estimate such treatment effects, one typically relies on data from …
Quasi-oracle estimation of heterogeneous treatment effects
Flexible estimation of heterogeneous treatment effects lies at the heart of many statistical
applications, such as personalized medicine and optimal resource allocation. In this article …
applications, such as personalized medicine and optimal resource allocation. In this article …
Gaussian process mixtures for estimating heterogeneous treatment effects
A Zaidi, S Mukherjee - arXiv preprint arXiv:1812.07153, 2018 - arxiv.org
We develop a Gaussian-process mixture model for heterogeneous treatment effect
estimation that leverages the use of transformed outcomes. The approach we will present …
estimation that leverages the use of transformed outcomes. The approach we will present …
Estimating heterogeneous treatment effects: Mutual information bounds and learning algorithms
Estimating heterogeneous treatment effects (HTE) from observational studies is rising in
importance due to the widespread accumulation of data in many fields. Due to the selection …
importance due to the widespread accumulation of data in many fields. Due to the selection …
Calibration error for heterogeneous treatment effects
Y Xu, S Yadlowsky - International Conference on Artificial …, 2022 - proceedings.mlr.press
Recently, many researchers have advanced data-driven methods for modeling
heterogeneous treatment effects (HTEs). Even still, estimation of HTEs is a difficult task …
heterogeneous treatment effects (HTEs). Even still, estimation of HTEs is a difficult task …
Nonparametric heterogeneous treatment effect estimation in repeated cross sectional designs
Identifying heterogeneity in a population's response to a health or policy intervention is
crucial for evaluating and informing policy decisions. We propose a novel heterogeneous …
crucial for evaluating and informing policy decisions. We propose a novel heterogeneous …
Augmented direct learning for conditional average treatment effect estimation with double robustness
H Meng, X Qiao - Electronic Journal of Statistics, 2022 - projecteuclid.org
Inferring the heterogeneous treatment effect is a fundamental problem in many applications.
In this paper, we focus on estimating the Conditional Average Treatment Effect (CATE), that …
In this paper, we focus on estimating the Conditional Average Treatment Effect (CATE), that …