Overlap-Adaptive Regularization for Conditional Average Treatment Effect Estimation
V. Melnychuk, D. Frauen, J. Schweisthal, S. Feuerriegel. Accepted at ICLR, 2026 (poster)
Publications
Peer-reviewed
2026
IGC-Net for Conditional Average Potential Outcome Estimation over Time
K. Hess, D. Frauen, V. Melnychuk, S. Feuerriegel. Accepted at ICLR, 2026 (poster)
GDR-learners: Orthogonal Learning of Generative Models for Potential Outcomes
V. Melnychuk, S. Feuerriegel. Accepted at ICLR, 2026 (poster)
Efficient and Sharp Off-Policy Learning under Unobserved Confounding
K. Hess, D. Frauen, V. Melnychuk, S. Feuerriegel. Accepted at ICLR, 2026 (poster)
An Orthogonal Learner for Individualized Outcomes in Markov Decision Processes
E. Javurek, V. Melnychuk, J. Schweisthal, K. Hess, D. Frauen, S. Feuerriegel. Accepted at ICLR, 2026 (poster)
Orthogonal Representation Learning for Estimating Causal Quantities
V. Melnychuk, D. Frauen, J. Schweisthal, S. Feuerriegel. Accepted at AISTATS, 2026 (oral presentation)
2025
Treatment Effect Estimation for Optimal Decision-Making
D. Frauen, V. Melnychuk, J. Schweisthal, M. van der Schaar, S. Feuerriegel. Accepted at NeurIPS, 2025 (poster)
Conformal Prediction for Causal Effects of Continuous Treatments
M. Schröder, D. Frauen, J. Schweisthal, K. Hess, V. Melnychuk, S. Feuerriegel. Accepted at NeurIPS, 2025 (poster)
Differentially Private Learners for Heterogeneous Treatment Effects
M. Schröder, V. Melnychuk, S. Feuerriegel. Accepted at ICLR, 2025 (poster)
2024
Quantifying Aleatoric Uncertainty of the Treatment Effect: A Novel Orthogonal Learner
V. Melnychuk, S. Feuerriegel, M. van der Schaar. Accepted at NeurIPS, 2024 (poster)
DiffPO: A Causal Diffusion Model for Predicting Potential Outcomes of Treatments
Y. Ma, V. Melnychuk, J. Schweisthal, S. Feuerriegel. Accepted at NeurIPS, 2024 (poster)
Fair Off-Policy Learning from Observational Data
D. Frauen, V. Melnychuk, S. Feuerriegel. Accepted at ICML, 2024 (poster)
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 (spotlight poster)
Bayesian Neural Controlled Differential Equations for Treatment Effect Estimation
K. Hess, V. Melnychuk, D. Frauen, S. Feuerriegel. Accepted at ICLR, 2024 (poster)
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 (poster)
2023
Sharp Bounds for Generalized Causal Sensitivity Analysis
D. Frauen, V. Melnychuk, S. Feuerriegel. Accepted at NeurIPS, 2023 (poster)
Reliable Off-Policy Learning for Dosage Combinations
J. Schweisthal, D. Frauen, V. Melnychuk, S. Feuerriegel. Accepted at NeurIPS, 2023 (poster)
Partial Counterfactual Identification of Continuous Outcomes with a Curvature Sensitivity Model
V. Melnychuk, D. Frauen, S. Feuerriegel. Accepted at NeurIPS, 2023 (spotlight poster)
Normalizing Flows for Interventional Density Estimation
V. Melnychuk, D. Frauen, S. Feuerriegel. Accepted at ICML, 2023 (poster)
Estimating Average Causal Effects from Patient Trajectories
D. Frauen, T. Hatt, V. Melnychuk, S. Feuerriegel. Accepted at AAAI, 2023 (poster)
2022
Causal Transformer for Estimating Counterfactual Outcomes
V. Melnychuk, D. Frauen, S. Feuerriegel. Accepted at ICML, 2022 (poster)
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
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