Early Detection of User Exits from Clickstream Data: A Markov Modulated Marked Point Process Model. - Tobias Hatt; Stefan Feuerriegel (WWW, 2020)

Estimating Average Treatment Effects via Orthogonal Regularization. - Tobias Hatt; Stefan Feuerriegel (CIKM, 2021)

AttDMM: An Attentive Deep Markov Model for Risk Scoring in Intensive Care Units. - Yilmazcan Özyurt; Mathias Kraus; Tobias Hatt; Stefan Feuerriegel (KDD, 2021)

Deconfounding Temporal Autoencoder: Estimating Treatment Effects over Time Using Noisy Proxies. - Milan Kuzmanovic; Tobias Hatt; Stefan Feuerriegel (ML4H@NeurIPS, 2021)

Interpretable Off-Policy Learning via Hyperbox Search. - Daniel Tschernutter; Tobias Hatt; Stefan Feuerriegel (ICML, 2022)

Generalizing off-policy learning under sample selection bias. - Tobias Hatt; Daniel Tschernutter; Stefan Feuerriegel (UAI, 2022)

A Deep Markov Model for Clickstream Analytics in Online Shopping. - Yilmazcan Özyurt; Tobias Hatt; Ce Zhang 001; Stefan Feuerriegel (WWW, 2022)

Combining Observational and Randomized Data for Estimating Heterogeneous Treatment Effects. - Tobias Hatt; Jeroen Berrevoets; Alicia Curth; Stefan Feuerriegel; Mihaela van der Schaar (CoRR, 2022)

DAPDAG: Domain Adaptation via Perturbed DAG Reconstruction. - Yanke Li; Tobias Hatt; Ioana Bica; Mihaela van der Schaar (CoRR, 2022)

Detecting User Exits from Online Behavior: A Duration-Dependent Latent State Model. - Tobias Hatt; Stefan Feuerriegel (CoRR, 2022)

Estimating Average Causal Effects from Patient Trajectories. - Dennis Frauen; Tobias Hatt; Valentyn Melnychuk; Stefan Feuerriegel (AAAI, 2023)

Estimating Conditional Average Treatment Effects with Missing Treatment Information. - Milan Kuzmanovic; Tobias Hatt; Stefan Feuerriegel (AISTATS, 2023)

Sequential Deconfounding for Causal Inference with Unobserved Confounders. - Tobias Hatt; Stefan Feuerriegel (CLeaR, 2024)

Causal Machine Learning for Cost-Effective Allocation of Development Aid. - Milan Kuzmanovic; Dennis Frauen; Tobias Hatt; Stefan Feuerriegel (KDD, 2024)