4 citations · 11 across the 9 of their papers we have counts for
5 papers · 1 filter
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…