Treatment Effect Estimation for Optimal Decision-Making
D. Frauen, V. Melnychuk, J. Schweisthal, M. van der Schaar, S. Feuerriegel. Accepted at NeurIPS, 2025
Publications
Peer-reviewed
2025
Conformal Prediction for Causal Effects of Continuous Treatments
M. Schröder, D. Frauen, J. Schweisthal, K. Hess, V. Melnychuk, S. Feuerriegel. Accepted at NeurIPS, 2025
Differentially Private Learners for Heterogeneous Treatment Effects
M. Schröder, V. Melnychuk, S. Feuerriegel. Accepted at ICLR, 2025
2024
Quantifying Aleatoric Uncertainty of the Treatment Effect: A Novel Orthogonal Learner
V. Melnychuk, S. Feuerriegel, M. van der Schaar. Accepted at NeurIPS, 2024
DiffPO: A Causal Diffusion Model for Predicting Potential Outcomes of Treatments
Y. Ma, V. Melnychuk, J. Schweisthal, S. Feuerriegel. Accepted at NeurIPS, 2024
Fair Off-Policy Learning from Observational Data
D. Frauen, V. Melnychuk, S. Feuerriegel. Accepted at ICML, 2024
Causal Machine Learning for Predicting Treatment Outcomes
S. Feuerriegel, D. Frauen, V. Melnychuk, J. Schweisthal, K. Hess, A. Curth, S. Bauer, N. Kilbertus, I. S. Kohane & M. van der Schaar. Accepted at Nature Medicine, 2024
Bounds on Representation-Induced Confounding Bias for Treatment Effect Estimation
V. Melnychuk, D. Frauen, S. Feuerriegel. Accepted at ICLR, 2024
Bayesian Neural Controlled Differential Equations for Treatment Effect Estimation
K. Hess, V. Melnychuk, D. Frauen, S. Feuerriegel. Accepted at ICLR, 2024
A Neural Framework for Generalized Causal Sensitivity Analysis
D. Frauen, F. Imrie, A. Curth, V. Melnychuk, S. Feuerriegel, M. van der Schaar. Accepted at ICLR, 2024
2023
Sharp Bounds for Generalized Causal Sensitivity Analysis
D. Frauen, V. Melnychuk, S. Feuerriegel. Accepted at NeurIPS, 2023
Reliable Off-Policy Learning for Dosage Combinations
J. Schweisthal, D. Frauen, V. Melnychuk, S. Feuerriegel. Accepted at NeurIPS, 2023
Partial Counterfactual Identification of Continuous Outcomes with a Curvature Sensitivity Model
V. Melnychuk, D. Frauen, S. Feuerriegel. Accepted at NeurIPS, 2023
Normalizing Flows for Interventional Density Estimation
V. Melnychuk, D. Frauen, S. Feuerriegel. Accepted at ICML, 2023
Estimating Average Causal Effects from Patient Trajectories
D. Frauen, T. Hatt, V. Melnychuk, S. Feuerriegel. Accepted at AAAI, 2023
2022
Causal Transformer for Estimating Counterfactual Outcomes
V. Melnychuk, D. Frauen, S. Feuerriegel. Accepted at ICML, 2022
2020
Knowledge Graph Entity Alignment with Graph Convolutional Networks: Lessons Learned
M. Berrendorf, E. Faerman, V. Melnychuk, V. Tresp, T. Seidl. Accepted at ECIR, 2020
Pre-prints
Overlap-Adaptive Regularization for Conditional Average Treatment Effect Estimation
V. Melnychuk, D. Frauen, J. Schweisthal, S. Feuerriegel. ArXiv preprint, 2025
GDR-learners: Orthogonal Learning of Generative Models for Potential Outcomes
V. Melnychuk, S. Feuerriegel. ArXiv preprint, 2025
Efficient and Sharp Off-Policy Learning under Unobserved Confounding
K. Hess, D. Frauen, V. Melnychuk, S. Feuerriegel. ArXiv preprint, 2025
Orthogonal Representation Learning for Estimating Causal Quantities
V. Melnychuk, D. Frauen, J. Schweisthal, S. Feuerriegel. ArXiv preprint, 2025
G-Transformer for Conditional Average Potential Outcome Estimation over Time
K. Hess, D. Frauen, V. Melnychuk, S. Feuerriegel. ArXiv preprint, 2024
Counterfactual Fairness for Predictions using Generative Adversarial Networks
Y. Ma, D. Frauen, V. Melnychuk, S. Feuerriegel. ArXiv preprint, 2023
Matching the Clinical Reality: Accurate OCT-Based Diagnosis From Few Labels
V. Melnychuk, E. Faerman, I. Manakov, T. Seidl. ArXiv preprint, 2020
Unsupervised Anomaly Detection for X-Ray Images
D. Davletshina, V. Melnychuk, V. Tran, H. Singla, M. Berrendorf, E. Faerman, M. Fromm, M. Schubert. ArXiv preprint, 2020