:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Arno, Henri, Frauen, Dennis, Javurek, Emil, Demeester, Thomas, Feuerriegel, Stefan
Format:	Preprint
Published:	2026
Subjects:	Machine Learning
Online Access:	https://arxiv.org/abs/2602.03517
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

An Orthogonal Learner for Individualized Outcomes in Markov Decision Processes
by: Javurek, Emil, et al.
Published: (2025)

Generalized Bayes for Causal Inference
by: Javurek, Emil, et al.
Published: (2026)

Foundation Models for Causal Inference via Prior-Data Fitted Networks
by: Ma, Yuchen, et al.
Published: (2025)

Orthogonal Learner for Estimating Heterogeneous Long-Term Treatment Effects
by: Ma, Haorui, et al.
Published: (2026)

Orthogonal Survival Learners for Estimating Heterogeneous Treatment Effects from Time-to-Event Data
by: Frauen, Dennis, et al.
Published: (2025)

Amortizing Causal Sensitivity Analysis via Prior Data-Fitted Networks
by: Javurek, Emil, et al.
Published: (2026)

Targeted Synthetic Control Method
by: Wang, Yuxin, et al.
Published: (2026)

Adaptive Experimentation for Censored Survival Outcomes
by: Wang, Yuxin, et al.
Published: (2026)

Meta-Learners for Partially-Identified Treatment Effects Across Multiple Environments
by: Schweisthal, Jonas, et al.
Published: (2024)

Bounds on Representation-Induced Confounding Bias for Treatment Effect Estimation
by: Melnychuk, Valentyn, et al.
Published: (2023)

Annotation-Assisted Learning of Treatment Policies From Multimodal Electronic Health Records
by: Arno, Henri, et al.
Published: (2025)

Orthogonal Representation Learning for Estimating Causal Quantities
by: Melnychuk, Valentyn, et al.
Published: (2025)

DeepBlip: Estimating Conditional Average Treatment Effects Over Time
by: Ma, Haorui, et al.
Published: (2025)

Overlap-Adaptive Regularization for Conditional Average Treatment Effect Estimation
by: Melnychuk, Valentyn, et al.
Published: (2025)

Bayesian Neural Controlled Differential Equations for Treatment Effect Estimation
by: Hess, Konstantin, et al.
Published: (2023)

LLM-Driven Treatment Effect Estimation Under Inference Time Text Confounding
by: Ma, Yuchen, et al.
Published: (2025)

Quantifying Aleatoric Uncertainty of the Treatment Effect: A Novel Orthogonal Learner
by: Melnychuk, Valentyn, et al.
Published: (2024)

Partial Counterfactual Identification of Continuous Outcomes with a Curvature Sensitivity Model
by: Melnychuk, Valentyn, et al.
Published: (2023)

Model-agnostic meta-learners for estimating heterogeneous treatment effects over time
by: Frauen, Dennis, et al.
Published: (2024)

Treatment Effect Estimation for Optimal Decision-Making
by: Frauen, Dennis, et al.
Published: (2025)

Constructing Confidence Intervals for Average Treatment Effects from Multiple Datasets
by: Wang, Yuxin, et al.
Published: (2024)

Learning Representations of Instruments for Partial Identification of Treatment Effects
by: Schweisthal, Jonas, et al.
Published: (2024)

Conformal Prediction for Causal Effects of Continuous Treatments
by: Schröder, Maresa, et al.
Published: (2024)

Causal Fairness under Unobserved Confounding: A Neural Sensitivity Framework
by: Schröder, Maresa, et al.
Published: (2023)

Differentially Private Learners for Heterogeneous Treatment Effects
by: Schröder, Maresa, et al.
Published: (2025)

Debiased neural operators for estimating functionals
by: Hess, Konstantin, et al.
Published: (2026)

Efficient and Sharp Off-Policy Learning under Unobserved Confounding
by: Hess, Konstantin, et al.
Published: (2025)

Causal Machine Learning for Cost-Effective Allocation of Development Aid
by: Kuzmanovic, Milan, et al.
Published: (2024)

IGC-Net for conditional average potential outcome estimation over time
by: Hess, Konstantin, et al.
Published: (2024)

Consistent End-to-End Estimation for Counterfactual Fairness
by: Ma, Yuchen, et al.
Published: (2023)

Nonparametric LLM Evaluation from Preference Data
by: Frauen, Dennis, et al.
Published: (2026)

Assessing the robustness of heterogeneous treatment effects in survival analysis under informative censoring
by: Wang, Yuxin, et al.
Published: (2025)

Overlap-weighted orthogonal meta-learner for treatment effect estimation over time
by: Hess, Konstantin, et al.
Published: (2025)

GDR-learners: Orthogonal Learning of Generative Models for Potential Outcomes
by: Melnychuk, Valentyn, et al.
Published: (2025)

Improving the Generation and Evaluation of Synthetic Data for Downstream Medical Causal Inference
by: Amad, Harry, et al.
Published: (2025)

A Neural Framework for Generalized Causal Sensitivity Analysis
by: Frauen, Dennis, et al.
Published: (2023)

From Text to Treatment Effects: A Meta-Learning Approach to Handling Text-Based Confounding
by: Arno, Henri, et al.
Published: (2024)

Detecting and Mitigating Group Bias in Heterogeneous Treatment Effects
by: Persson, Joel, et al.
Published: (2026)

ORTHOBO: Orthogonal Bayesian Hyperparameter Optimization
by: Schröder, Maresa, et al.
Published: (2026)

Metalearners for Ranking Treatment Effects
by: Vanderschueren, Toon, et al.
Published: (2024)