4 citations · 11 across the 9 of their papers we have counts for
11 papers
Causal Machine Learning for Cost-Effective Allocation of Development Aid
Milan Kuzmanovic, Dennis Frauen, Tobias Hatt +1
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 r…
Detecting User Exits from Online Behavior: A Duration-Dependent Latent State Model
Tobias Hatt, Stefan Feuerriegel
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 hi…
Interpretable Off-Policy Learning via Hyperbox Search
Daniel Tschernutter, Tobias Hatt, Stefan Feuerriegel
Personalized treatment decisions have become an integral part of modern medicine. Thereby, the aim is to make treatment decisions based on individual patient characteristics. Numer…
Estimating Conditional Average Treatment Effects with Missing Treatment Information
Milan Kuzmanovic, Tobias Hatt, Stefan Feuerriegel
Estimating conditional average treatment effects (CATE) is challenging, especially when treatment information is missing. Although this is a widespread problem in practice, CATE es…
Estimating average causal effects from patient trajectories
Dennis Frauen, Tobias Hatt, Valentyn Melnychuk +1
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 controll…
Combining Observational and Randomized Data for Estimating Heterogeneous Treatment Effects
Tobias Hatt, Jeroen Berrevoets, Alicia Curth +2
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 ob…
Deconfounding Temporal Autoencoder: Estimating Treatment Effects over Time Using Noisy Proxies
Milan Kuzmanovic, Tobias Hatt, Stefan Feuerriegel
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 al…
Generalizing Off-Policy Learning under Sample Selection Bias
Tobias Hatt, Daniel Tschernutter, Stefan Feuerriegel
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, st…
Sequential Deconfounding for Causal Inference with Unobserved Confounders
Tobias Hatt, Stefan Feuerriegel
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 d…
AttDMM: An Attentive Deep Markov Model for Risk Scoring in Intensive Care Units
Yilmazcan Özyurt, Mathias Kraus, Tobias Hatt +1
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 e…
Estimating Average Treatment Effects via Orthogonal Regularization
Tobias Hatt, Stefan Feuerriegel
Decision-making often requires accurate estimation of treatment effects from observational data. This is challenging as outcomes of alternative decisions are not observed and have…