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pro vyhledávání: '"Künzel, Sören R."'
Regression trees and their ensemble methods are popular methods for nonparametric regression: they combine strong predictive performance with interpretable estimators. To improve their utility for locally smooth response surfaces, we study regression
Externí odkaz:
http://arxiv.org/abs/1906.06463
Estimating heterogeneous treatment effects has become increasingly important in many fields and life and death decisions are now based on these estimates: for example, selecting a personalized course of medical treatment. Recently, a variety of proce
Externí odkaz:
http://arxiv.org/abs/1811.02833
Autor:
Künzel, Sören R., Stadie, Bradly C., Vemuri, Nikita, Ramakrishnan, Varsha, Sekhon, Jasjeet S., Abbeel, Pieter
We develop new algorithms for estimating heterogeneous treatment effects, combining recent developments in transfer learning for neural networks with insights from the causal inference literature. By taking advantage of transfer learning, we are able
Externí odkaz:
http://arxiv.org/abs/1808.07804
There is growing interest in estimating and analyzing heterogeneous treatment effects in experimental and observational studies. We describe a number of meta-algorithms that can take advantage of any supervised learning or regression method in machin
Externí odkaz:
http://arxiv.org/abs/1706.03461
We were trying to understand the analysis provided by Kneip (1994, Ordered Linear Smoothers). In particular we wanted to persuade ourselves that his results imply the oracle inequality stated by Tsybakov (2014, Lecture 8). This note contains our rewo
Externí odkaz:
http://arxiv.org/abs/1405.1744
Publikováno v:
Proceedings of the National Academy of Sciences of the United States of America, 2019 Mar 01. 116(10), 4156-4165.
Externí odkaz:
https://www.jstor.org/stable/26683078
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