Publications

(* indicates equal contribution)

Peer-reviewed

Frauen, D.*, Schröder, M.*, Hess, K. & Feuerriegel, S. (2025). Orthogonal Survival Learners for Estimating Heterogeneous Treatment Effects from Time-to-Event Data. In NeurIPS 2025. arXiv

Ma, Y., Frauen, D., Schweisthal, J. & Feuerriegel, S. (2025). Treatment Effect Estimation for Optimal Decision-Making. In NeurIPS 2025. arXiv

Frauen, D., Melnychuk, V., Schweisthal, J., van der Schaar, M. & Feuerriegel, S. (2025). Treatment Effect Estimation for Optimal Decision-Making. In NeurIPS 2025. arXiv

Schröder, M., Frauen, D., Schweisthal, J., Hess, K., Melnychuk, M. & Feuerriegel, S. (2025). Conformal Prediction for Causal Effects of Continuous Treatments. In NeurIPS 2025. arXiv

Amad, H., Qian, Z., Frauen, D., Piskorz, J., Feuerriegel, S. & van der Schaar, M. (2025). Improving the Generation and Evaluation of Synthetic Data for Downstream Medical Causal Inference. In NeurIPS 2025. arXiv

Kern, C., Fischer, U.A., Schweisthal, J., Frauen, D., Ghani, R., Feuerriegel, S., van der Schaar, M. & Kreuter F. (2025). Algorithms for reliable decision-making need causal reasoning. In Nature Computational Science. PDF

Schweisthal, J., Frauen, D., Schröder, M., Hess, K., Kilbertus, N. & Feuerriegel, S. (2025). Learning Representations of Instruments for Partial Identification of Treatment Effects. In ICML 2025. PDF

Frauen, D., Hess, K. & Feuerriegel, S. (2025). Model-agnostic meta-learners for estimating heterogeneous treatment effects over time. In ICLR 2025. PDF

Wang, Y., Schröder, M., Frauen, D., Schweisthal, J., Hess, K. & Feuerriegel, S. (2025). Constructing Confidence Intervals for Average Treatment Effects from Multiple Datasets. In ICLR 2025. PDF

Feuerriegel, S., Frauen, D., Melnychuk, M., Schweisthal, J., Hess, K., Curth, A., Bauer, S., Kilbertus, N., Kohane, I.S. & van der Schaar, M. (2024). Causal Machine Learning for Predicting Treatment Outcomes. Nature Medicine. PDF

Schweisthal, J.*, Frauen, D.*, van der Schaar, M. & Feuerriegel, S. (2024). Meta-Learners for Partially-Identified Treatment Effects Across Multiple Environments. In ICML 2024. PDF arXiv

Frauen, D., Melnychuk, M., & Feuerriegel, S. (2024). Fair Off-Policy Learning from Observational Data. In ICML 2024. PDF arXiv

Kuzmanovic, M., Frauen, D., Hatt, T. & Feuerriegel, S. (2024). Causal Machine Learning for Cost-Effective Allocation of Development Aid. In KDD 2024. PDF arXiv

Frauen, D., Imrie, F., Curth, A., Melnychuk, M., Feuerriegel, S. & van der Schaar, M. (2024). A Neural Framework for Generalized Causal Sensitivity Analysis. In ICLR 2024. PDF arXiv

Hess, K., Melnychuk, M., Frauen, D. & Feuerriegel, S. (2024). Bayesian Neural Controlled Differential Equations for Treatment Effect Estimation. In ICLR 2024. PDF arXiv

Schröder, M., Frauen, D. & Feuerriegel, S. (2024). Causal Fairness under Unobserved Confounding: A Neural Sensitivity Framework. In ICLR 2024. PDF

Melnychuk, M., Frauen, D. & Feuerriegel, S. (2024). Bounds on Representation-Induced Confounding Bias for Treatment Effect Estimation. In ICLR 2024. PDF arXiv

Frauen, D., Melnychuk, M., & Feuerriegel, S. (2023). Sharp Bounds for Generalized Causal Sensitivity Analysis. In NeurIPS 2023. PDF arXiv

Melnychuk, M., Frauen, D. & Feuerriegel, S. (2023). Partial Counterfactual Identification of Continuous Outcomes with a Curvature Sensitivity Model. In NeurIPS 2023. PDF arXiv

Schweisthal, J., Frauen, D., Melnychuk, M. & Feuerriegel, S. (2023). Reliable Off-Policy Learning for Dosage Combinations. In NeurIPS 2023. PDF arXiv

Melnychuk, M., Frauen, D. & Feuerriegel, S. (2023). Normalizing Flows for Interventional Density Estimation. In ICML 2023. PDF arXiv

Frauen, D. & Feuerriegel, S. (2023). Estimating Individual Treatment Effects under Unobserved Confounding using Binary Instruments. In ICLR 2023. PDF arXiv

Frauen, D., Melnychuk, M. & Feuerriegel, S. (2023). Estimating Average Causal Effects from Patient Trajectories. In AAAI 2023. PDF arXiv

Melnychuk, M., Frauen, D. & Feuerriegel, S. (2022). Causal Transformer for Estimating Counterfactual Outcomes. In ICML 2022. PDF arXiv

Preprints

Ma, Y.*, Frauen, D.*, Javurek, E. & Feuerriegel, S. (2025). Foundation Models for Causal Inference via Prior-Data Fitted Networks. arXiv

Hess, K., Frauen, D., Melnychuk, M. & Feuerriegel, S. (2024). Efficient and Sharp Off-Policy Learning under Unobserved Confounding. arXiv

Melnychuk, M., Frauen, D., Schweisthal, J. & Feuerriegel, S. (2025). Orthogonal Representation Learning for Estimating Causal Quantities. arXiv

Hess, K., Frauen, D., Melnychuk, M. & Feuerriegel, S. (2024). G-Transformer for Conditional Average Potential Outcome Estimation over Time. arXiv

Ma, Y., Frauen, D., Melnychuk, M. & Feuerriegel, S. (2024). Counterfactual Fairness for Predictions using Generative Adversarial Networks. arXiv