1 code implementation • 30 Jan 2024 • Milan Kuzmanovic, Dennis Frauen, Tobias Hatt, Stefan Feuerriegel
Then, we demonstrate our framework using real-world HIV data.
1 code implementation • 8 Aug 2022 • Tobias Hatt, Stefan Feuerriegel
In this work, we develop a duration-dependent hidden Markov model.
1 code implementation • 4 Mar 2022 • Daniel Tschernutter, Tobias Hatt, Stefan Feuerriegel
Using a simulation study, we demonstrate that our algorithm outperforms state-of-the-art methods from interpretable off-policy learning in terms of regret.
1 code implementation • 2 Mar 2022 • Dennis Frauen, Tobias Hatt, Valentyn Melnychuk, Stefan Feuerriegel
In medical practice, treatments are selected based on the expected causal effects on patient outcomes.
1 code implementation • 2 Mar 2022 • Milan Kuzmanovic, Tobias Hatt, Stefan Feuerriegel
Although this is a widespread problem in practice, CATE estimation with missing treatments has received little attention.
1 code implementation • 25 Feb 2022 • Tobias Hatt, Jeroen Berrevoets, Alicia Curth, Stefan Feuerriegel, Mihaela van der Schaar
While observational data is confounded, randomized data is unconfounded, but its sample size is usually too small to learn heterogeneous treatment effects.
1 code implementation • 6 Dec 2021 • Milan Kuzmanovic, Tobias Hatt, Stefan Feuerriegel
To this end, we develop the Deconfounding Temporal Autoencoder, a novel method that leverages observed noisy proxies to learn a hidden embedding that reflects the true hidden confounders.
no code implementations • 2 Dec 2021 • Tobias Hatt, Daniel Tschernutter, Stefan Feuerriegel
Since training data is often not representative of the target population, standard policy learning methods may yield policies that do not generalize target population.
1 code implementation • 16 Apr 2021 • Tobias Hatt, Stefan Feuerriegel
In this paper, we develop the Sequential Deconfounder, a method that enables estimating individualized treatment effects over time in presence of unobserved confounders.
1 code implementation • 9 Feb 2021 • Yilmazcan Özyurt, Mathias Kraus, Tobias Hatt, Stefan Feuerriegel
In this work, we propose a novel generative deep probabilistic model for real-time risk scoring in ICUs.
1 code implementation • 21 Jan 2021 • Tobias Hatt, Stefan Feuerriegel
In this paper, we propose a novel regularization framework for estimating average treatment effects that exploits unconfoundedness.
no code implementations • 1 Jan 2021 • Tobias Hatt, Stefan Feuerriegel
Based on our regularization framework, we develop deep orthogonal networks for unconfounded treatments (DONUT) which learn outcomes that are orthogonal to the treatment assignment.