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pro vyhledávání: '"Hatt, Tobias"'
The Sustainable Development Goals (SDGs) of the United Nations provide a blueprint of a better future by 'leaving no one behind', and, to achieve the SDGs by 2030, poor countries require immense volumes of development aid. In this paper, we develop a
Externí odkaz:
http://arxiv.org/abs/2401.16986
Autor:
Hatt, Tobias, Feuerriegel, Stefan
In order to steer e-commerce users towards making a purchase, marketers rely upon predictions of when users exit without purchasing. Previously, such predictions were based upon hidden Markov models (HMMs) due to their ability of modeling latent shop
Externí odkaz:
http://arxiv.org/abs/2208.03937
Personalized treatment decisions have become an integral part of modern medicine. Thereby, the aim is to make treatment decisions based on individual patient characteristics. Numerous methods have been developed for learning such policies from observ
Externí odkaz:
http://arxiv.org/abs/2203.02473
In medical practice, treatments are selected based on the expected causal effects on patient outcomes. Here, the gold standard for estimating causal effects are randomized controlled trials; however, such trials are costly and sometimes even unethica
Externí odkaz:
http://arxiv.org/abs/2203.01228
Estimating conditional average treatment effects (CATE) is challenging, especially when treatment information is missing. Although this is a widespread problem in practice, CATE estimation with missing treatments has received little attention. In thi
Externí odkaz:
http://arxiv.org/abs/2203.01422
Autor:
Hatt, Tobias, Berrevoets, Jeroen, Curth, Alicia, Feuerriegel, Stefan, van der Schaar, Mihaela
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 observational studies or randomized experiments. Currently, most existin
Externí odkaz:
http://arxiv.org/abs/2202.12891
Publikováno v:
Proceedings of Machine Learning for Health, PMLR 158:143-155, 2021
Estimating individualized treatment effects (ITEs) from observational data is crucial for decision-making. In order to obtain unbiased ITE estimates, a common assumption is that all confounders are observed. However, in practice, it is unlikely that
Externí odkaz:
http://arxiv.org/abs/2112.03013
Learning personalized decision policies that generalize to the target population is of great relevance. Since training data is often not representative of the target population, standard policy learning methods may yield policies that do not generali
Externí odkaz:
http://arxiv.org/abs/2112.01387
Autor:
Hatt, Tobias, Feuerriegel, Stefan
Using observational data to estimate the effect of a treatment is a powerful tool for decision-making when randomized experiments are infeasible or costly. However, observational data often yields biased estimates of treatment effects, since treatmen
Externí odkaz:
http://arxiv.org/abs/2104.09323
Clinical practice in intensive care units (ICUs) requires early warnings when a patient's condition is about to deteriorate so that preventive measures can be undertaken. To this end, prediction algorithms have been developed that estimate the risk o
Externí odkaz:
http://arxiv.org/abs/2102.04702