322 citations · 381 across the 26 of their papers we have counts for
7 papers · 1 filter
An Orthogonal Learner for Individualized Outcomes in Markov Decision Processes
Emil Javurek, Valentyn Melnychuk, Jonas Schweisthal +3
Predicting individualized potential outcomes in sequential decision-making is central for optimizing therapeutic decisions in personalized medicine (e.g., which dosing sequence to…
Overlap-Adaptive Regularization for Conditional Average Treatment Effect Estimation
Valentyn Melnychuk, Dennis Frauen, Jonas Schweisthal +1
The conditional average treatment effect (CATE) is widely used in personalized medicine to inform therapeutic decisions. However, state-of-the-art methods for CATE estimation (so-c…
GDR-learners: Orthogonal Learning of Generative Models for Potential Outcomes
Valentyn Melnychuk, Stefan Feuerriegel
Various deep generative models have been proposed to estimate potential outcomes distributions from observational data. However, none of them have the favorable theoretical propert…
Treatment Effect Estimation for Optimal Decision-Making
Dennis Frauen, Valentyn Melnychuk, Jonas Schweisthal +2
Decision-making across various fields, such as medicine, heavily relies on conditional average treatment effects (CATEs). Practitioners commonly make decisions by checking whether…
Differentially Private Learners for Heterogeneous Treatment Effects
Maresa Schröder, Valentyn Melnychuk, Stefan Feuerriegel
Patient data is widely used to estimate heterogeneous treatment effects and thus understand the effectiveness and safety of drugs. Yet, patient data includes highly sensitive infor…
Efficient and Sharp Off-Policy Learning under Unobserved Confounding
Konstantin Hess, Dennis Frauen, Valentyn Melnychuk +1
We develop a novel method for personalized off-policy learning in scenarios with unobserved confounding. Thereby, we address a key limitation of standard policy learning: standard…
Orthogonal Representation Learning for Estimating Causal Quantities
Valentyn Melnychuk, Dennis Frauen, Jonas Schweisthal +1
End-to-end representation learning has become a powerful tool for estimating causal quantities from high-dimensional observational data, but its efficiency remained unclear. Here,…