Results 31 to 40 of about 98,771 (295)
Outcome regression-based estimation of conditional average treatment effect
Annals of the Institute of Statistical Mathematics, 2022 The research is about a systematic investigation on the following issues. First, we construct different outcome regression-based estimators for conditional average treatment effect under, respectively, true (oracle), parametric, nonparametric and semiparametric dimension reduction structure.
Li, Lu, Zhou, Niwen, Zhu, Lixing
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A Meta-Learning Approach for Estimating Heterogeneous Treatment Effects Under Hölder Continuity
MathematicsEstimating heterogeneous treatment effects plays a vital role in many statistical applications, such as precision medicine and precision marketing. In this paper, we propose a novel meta-learner, termed RXlearner for estimating the conditional average ...
Zhihao Zhao, Congyang Zhou
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Frontiers in Genetics, 2020 Treatment response is heterogeneous. However, the classical methods treat the treatment response as homogeneous and estimate the average treatment effects. The traditional methods are difficult to apply to precision oncology. Artificial intelligence (AI)
Qiyang Ge +7 more
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What are the treatment effects of a work-first participation programme on young unemployed people in the Netherlands? [PDF]
Panoeconomicus, 2019 This paper evaluates the effects of the employment programme on young unemployed people in the Netherlands. The effectiveness of the programme is measured by probability of both re-employment and participation within the regular educational ...
Južnik-Rotar Laura
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Economics LettersWe propose a model averaging approach, combined with a partition and matching method to estimate the conditional average treatment effects under heteroskedastic error settings. The proposed approach has asymptotic optimality and consistency of weights and estimator. Numerical studies show that our method has good finite-sample performances.
Shi, Pengfei, Zhang, Xinyu, Zhong, Wei
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MathematicsWe present a comprehensive framework for estimating heterogeneous treatment effects and evaluating decision-making policies in high-dimensional settings.
Jong-Min Kim
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Information Theoretic Causal Effect Quantification
Entropy, 2019 Modelling causal relationships has become popular across various disciplines. Most common frameworks for causality are the Pearlian causal directed acyclic graphs (DAGs) and the Neyman-Rubin potential outcome framework.
Aleksander Wieczorek, Volker Roth
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BENK: The Beran Estimator with Neural Kernels for Estimating the Heterogeneous Treatment Effect
AlgorithmsA method for estimating the conditional average treatment effect under the condition of censored time-to-event data, called BENK (the Beran Estimator with Neural Kernels), is proposed.
Stanislav Kirpichenko +3 more
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Labor Supply and Conditional Cash Transfer: Evidences from Tekoporã Program
Población y Desarrollo, 2019 This article searches understand the effects caused by the Conditional Cash Transfer Program Tekoporã of Paraguay in labor supply of participants. To achieve this purpose, a group of program participants was identified and, using the Propensity Score ...
Juan Carlos Núñez Guerrero
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Molecular Oncology, EarlyView.This study shows that lung adenocarcinomas exploit developmental branching morphogenesis to acquire a therapy resistant basal‐like tumour cell state. This process was found to be regulated by combined TP53 loss‐of‐function and type‐I interferon signalling, identifying a novel axis for biomarker and therapeutic target discovery.
Kamila J Bienkowska +13 more
wiley +1 more source