Dennis Frauen

Dennis Frauen

PhD Student in Causal Machine Learning

Publications

You can also find all papers on my Google Scholar profile.

(* indicates equal contribution)

Peer-reviewed

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. 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. arXiv

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

Frauen, D., Hess, K. & Feuerriegel, S. (2024). Model-agnostic meta-learners for estimating heterogeneous treatment effects over time. arXiv

Schweisthal, J., Frauen, D., Schröder, M., Hess, K., Kilbertus, N. & Feuerriegel, S. 2024. Learning Representations of Instruments for Partial Identification of Treatment Effects. arXiv

Schröder, M., Frauen, D., Schweisthal, J., Hess, K., Melnychuk, M. & Feuerriegel, S. (2024). Conformal Prediction for Causal Effects of Continuous Treatments. 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