322 citations · 381 across the 26 of their papers we have counts for
30 papers
OrthoGen: A Generative Orthogonal Learner for Time-Varying Treatments
Tomàs Garriga, Valentyn Melnychuk, Konstantin Hess +4
Estimating conditional distributional potential outcomes (CDPOs) over time is important in medicine (e.g., to estimate patient-specific risks under different treatment sequences).…
ConfoundingSHAP: Quantifying confounding strength in causal inference
Marie Brockschmidt, Santo M. A. R. Thies, Maresa Schröder +5
In causal inference, confounders are variables that influence both treatment decisions and outcomes. However, unlike as in randomized clinical trials, the treatment assignment mech…
Orthogonal Learner for Estimating Heterogeneous Long-Term Treatment Effects
Haorui Ma, Dennis Frauen, Valentyn Melnychuk +1
Estimation of heterogeneous long-term treatment effects (HLTEs) is relevant for personalized decision-making in marketing, economics, and medicine, where short-term observational d…
Frequentist Consistency of Prior-Data Fitted Networks for Causal Inference
Valentyn Melnychuk, Vahid Balazadeh, Stefan Feuerriegel +1
Foundation models based on prior-data fitted networks (PFNs) have shown strong empirical performance in causal inference by framing the task as an in-context learning problem. Howe…
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,…
Quantifying Aleatoric Uncertainty of the Treatment Effect: A Novel Orthogonal Learner
Valentyn Melnychuk, Stefan Feuerriegel, Mihaela van der Schaar
Estimating causal quantities from observational data is crucial for understanding the safety and effectiveness of medical treatments. However, to make reliable inferences, medical…
DiffPO: A causal diffusion model for learning distributions of potential outcomes
Yuchen Ma, Valentyn Melnychuk, Jonas Schweisthal +1
Predicting potential outcomes of interventions from observational data is crucial for decision-making in medicine, but the task is challenging due to the fundamental problem of cau…
Causal machine learning for predicting treatment outcomes
Stefan Feuerriegel, Dennis Frauen, Valentyn Melnychuk +7
Causal machine learning (ML) offers flexible, data-driven methods for predicting treatment outcomes including efficacy and toxicity, thereby supporting the assessment and safety of…
Conformal Prediction for Causal Effects of Continuous Treatments
Maresa Schröder, Dennis Frauen, Jonas Schweisthal +3
Uncertainty quantification of causal effects is crucial for safety-critical applications such as personalized medicine. A powerful approach for this is conformal prediction, which…
IGC-Net for conditional average potential outcome estimation over time
Konstantin Hess, Dennis Frauen, Valentyn Melnychuk +1
Estimating potential outcomes for treatments over time based on observational data is important for personalized decision-making in medicine. However, many existing methods for thi…
A Neural Framework for Generalized Causal Sensitivity Analysis
Dennis Frauen, Fergus Imrie, Alicia Curth +3
Unobserved confounding is common in many applications, making causal inference from observational data challenging. As a remedy, causal sensitivity analysis is an important tool to…
Bounds on Representation-Induced Confounding Bias for Treatment Effect Estimation
Valentyn Melnychuk, Dennis Frauen, Stefan Feuerriegel
State-of-the-art methods for conditional average treatment effect (CATE) estimation make widespread use of representation learning. Here, the idea is to reduce the variance of the…
Consistent End-to-End Estimation for Counterfactual Fairness
Yuchen Ma, Valentyn Melnychuk, Dennis Frauen +1
Fairness in predictions is of direct importance in practice due to legal, ethical, and societal reasons. This is often accomplished through counterfactual fairness, which ensures t…
Bayesian Neural Controlled Differential Equations for Treatment Effect Estimation
Konstantin Hess, Valentyn Melnychuk, Dennis Frauen +1
Treatment effect estimation in continuous time is crucial for personalized medicine. However, existing methods for this task are limited to point estimates of the potential outcome…
Partial Counterfactual Identification of Continuous Outcomes with a Curvature Sensitivity Model
Valentyn Melnychuk, Dennis Frauen, Stefan Feuerriegel
Counterfactual inference aims to answer retrospective "what if" questions and thus belongs to the most fine-grained type of inference in Pearl's causality ladder. Existing methods…
Reliable Off-Policy Learning for Dosage Combinations
Jonas Schweisthal, Dennis Frauen, Valentyn Melnychuk +1
Decision-making in personalized medicine such as cancer therapy or critical care must often make choices for dosage combinations, i.e., multiple continuous treatments. Existing wor…
Sharp Bounds for Generalized Causal Sensitivity Analysis
Dennis Frauen, Valentyn Melnychuk, Stefan Feuerriegel
Causal inference from observational data is crucial for many disciplines such as medicine and economics. However, sharp bounds for causal effects under relaxations of the unconfoun…
Fair Off-Policy Learning from Observational Data
Dennis Frauen, Valentyn Melnychuk, Stefan Feuerriegel
Algorithmic decision-making in practice must be fair for legal, ethical, and societal reasons. To achieve this, prior research has contributed various approaches that ensure fairne…