Cover Image

An Orthogonal Learner for Individualized Outcomes in Markov Decision Processes

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Javurek, Emil, Melnychuk, Valentyn, Schweisthal, Jonas, Hess, Konstantin, Frauen, Dennis, Feuerriegel, Stefan
Format:	Preprint
Published:	2025
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

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

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

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

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

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

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

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

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

Rank-Learner: Orthogonal Ranking of Treatment Effects
by: Arno, Henri, et al.
Published: (2026)

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

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

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

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

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

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

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

DiffPO: A causal diffusion model for learning distributions of potential outcomes
by: Ma, Yuchen, et al.
Published: (2024)

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

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

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

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

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

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

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

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

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

Causal machine learning for predicting treatment outcomes
by: Feuerriegel, Stefan, et al.
Published: (2024)

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

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

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

Stabilized Neural Prediction of Potential Outcomes in Continuous Time
by: Hess, Konstantin, et al.
Published: (2024)

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

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

A Diffusion-Based Method for Learning the Multi-Outcome Distribution of Medical Treatments
by: Ma, Yuchen, et al.
Published: (2025)

Frequentist Consistency of Prior-Data Fitted Networks for Causal Inference
by: Melnychuk, Valentyn, et al.
Published: (2026)

ConfoundingSHAP: Quantifying confounding strength in causal inference
by: Brockschmidt, Marie, et al.
Published: (2026)

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

Causal methods for LLM development and evaluation
by: Frauen, Dennis, et al.
Published: (2026)

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

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