| Combining observational and randomized data for estimating heterogeneous treatment effects T Hatt, J Berrevoets, A Curth, S Feuerriegel, M van der Schaar arXiv preprint arXiv:2202.12891, 2022 | 49 | 2022 |
| Sequential deconfounding for causal inference with unobserved confounders T Hatt, S Feuerriegel Conference on Causal Learning and Reasoning (CLeaR), 2024 | 48 | 2024 |
| AttDMM: An Attentive Deep Markov Model for Risk Scoring in Intensive Care Units Y Oezyurt, M Kraus, T Hatt, S Feuerriegel International Conference on Knowledge Discovery & Data Mining (KDD), 2021 | 48 | 2021 |
| Early detection of user exits from clickstream data: A Markov modulated marked point process model T Hatt, S Feuerriegel Proceedings of The Web Conference 2020, 1671-1681, 2020 | 47 | 2020 |
| Estimating average treatment effects via orthogonal regularization T Hatt, S Feuerriegel International Conference on Information and Knowledge Management (CIKM), 2021 | 41 | 2021 |
| Deconfounding Temporal Autoencoder: estimating treatment effects over time using noisy proxies M Kuzmanovic, T Hatt, S Feuerriegel Machine learning for health, 143-155, 2021 | 35 | 2021 |
| Generalizing Off-Policy Learning under Sample Selection Bias T Hatt, D Tschernutter, S Feuerriegel Conference on Uncertainty in Artificial Intelligence (UAI), 2022 | 34 | 2022 |
| Estimating average causal effects from patient trajectories D Frauen, T Hatt, V Melnychuk, S Feuerriegel AAAI conference on artificial intelligence, 2023 | 32 | 2023 |
| A Deep Markov Model for Clickstream Analytics in Online Shopping Y Özyurt, T Hatt, C Zhang, S Feuerriegel The Web Conference (WebConf 2022), 2022 | 24 | 2022 |
| Estimating conditional average treatment effects with missing treatment information M Kuzmanovic, T Hatt, S Feuerriegel International Conference on Artificial Intelligence and Statistics (AISTATS), 2023 | 18 | 2023 |
| Causal Machine Learning for Cost-Effective Allocation of Development Aid M Kuzmanovic, D Frauen, T Hatt, S Feuerriegel Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and …, 2024 | 15 | 2024 |
| Interpretable Off-Policy Learning via Hyperbox Search D Tschernutter, T Hatt, S Feuerriegel International Conference on Machine Learning (ICML), 2022 | 12 | 2022 |
| Detecting user exits from online behavior: A duration-dependent latent state model T Hatt, S Feuerriegel arXiv preprint arXiv:2208.03937, 2022 | 4 | 2022 |
| Causal AI in Personalised Healthcare T Hatt Dimensions of Intelligent Analytics for Smart Digital Health Solutions, 2024 | 2 | 2024 |
| DAPDAG: Domain Adaptation via Perturbed DAG Reconstruction Y Li, T Hatt, I Bica, M van der Schaar arXiv preprint arXiv:2208.01373, 2022 | | 2022 |
| Personalized Medicine using Real-World Data: a Causal Machine Learning Approach T Hatt ETH Zurich, 2022 | | 2022 |
| An Essay on Frictions in Stochastic Portfolio Theory T Hatt Available at SSRN 4642189, 2016 | | 2016 |
| Generalizing Off-Policy Learning under Sample Selection Bias (Supplementary material) T Hatt, D Tschernutter, S Feuerriegel | | |
| Estimating Treatment Effects via Orthogonal Regularization T Hatt, S Feuerriegel | | |
Stefan FeuerriegelProfessor, LMU MunichVerified email at lmu.de
