Model-agnostic meta-learners for estimating heterogeneous treatment effects over time
Fuente:
arXiv
Saved in:
| Main Authors: | Frauen, Dennis, Hess, Konstantin, Feuerriegel, Stefan |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Overlap-weighted orthogonal meta-learner for treatment effect estimation over time
by: Hess, Konstantin, et al.
Published: (2025)
by: Hess, Konstantin, et al.
Published: (2025)
IGC-Net for conditional average potential outcome estimation over time
by: Hess, Konstantin, et al.
Published: (2024)
by: Hess, Konstantin, et al.
Published: (2024)
Debiased neural operators for estimating functionals
by: Hess, Konstantin, et al.
Published: (2026)
by: Hess, Konstantin, et al.
Published: (2026)
Efficient and Sharp Off-Policy Learning under Unobserved Confounding
by: Hess, Konstantin, et al.
Published: (2025)
by: Hess, Konstantin, et al.
Published: (2025)
Bayesian Neural Controlled Differential Equations for Treatment Effect Estimation
by: Hess, Konstantin, et al.
Published: (2023)
by: Hess, Konstantin, et al.
Published: (2023)
Orthogonal Survival Learners for Estimating Heterogeneous Treatment Effects from Time-to-Event Data
by: Frauen, Dennis, et al.
Published: (2025)
by: Frauen, Dennis, et al.
Published: (2025)
Assessing the robustness of heterogeneous treatment effects in survival analysis under informative censoring
by: Wang, Yuxin, et al.
Published: (2025)
by: Wang, Yuxin, et al.
Published: (2025)
Constructing Confidence Intervals for Average Treatment Effects from Multiple Datasets
by: Wang, Yuxin, et al.
Published: (2024)
by: Wang, Yuxin, et al.
Published: (2024)
Targeted Synthetic Control Method
by: Wang, Yuxin, et al.
Published: (2026)
by: Wang, Yuxin, et al.
Published: (2026)
An Orthogonal Learner for Individualized Outcomes in Markov Decision Processes
by: Javurek, Emil, et al.
Published: (2025)
by: Javurek, Emil, et al.
Published: (2025)
Partial Counterfactual Identification of Continuous Outcomes with a Curvature Sensitivity Model
by: Melnychuk, Valentyn, et al.
Published: (2023)
by: Melnychuk, Valentyn, et al.
Published: (2023)
Stabilized Neural Prediction of Potential Outcomes in Continuous Time
by: Hess, Konstantin, et al.
Published: (2024)
by: Hess, Konstantin, et al.
Published: (2024)
Learning Representations of Instruments for Partial Identification of Treatment Effects
by: Schweisthal, Jonas, et al.
Published: (2024)
by: Schweisthal, Jonas, et al.
Published: (2024)
Conformal Prediction for Causal Effects of Continuous Treatments
by: Schröder, Maresa, et al.
Published: (2024)
by: Schröder, Maresa, et al.
Published: (2024)
Bounds on Representation-Induced Confounding Bias for Treatment Effect Estimation
by: Melnychuk, Valentyn, et al.
Published: (2023)
by: Melnychuk, Valentyn, et al.
Published: (2023)
Foundation Models for Causal Inference via Prior-Data Fitted Networks
by: Ma, Yuchen, et al.
Published: (2025)
by: Ma, Yuchen, et al.
Published: (2025)
Causal Fairness under Unobserved Confounding: A Neural Sensitivity Framework
by: Schröder, Maresa, et al.
Published: (2023)
by: Schröder, Maresa, et al.
Published: (2023)
DeepBlip: Estimating Conditional Average Treatment Effects Over Time
by: Ma, Haorui, et al.
Published: (2025)
by: Ma, Haorui, et al.
Published: (2025)
Causal machine learning for predicting treatment outcomes
by: Feuerriegel, Stefan, et al.
Published: (2024)
by: Feuerriegel, Stefan, et al.
Published: (2024)
Causal Machine Learning for Cost-Effective Allocation of Development Aid
by: Kuzmanovic, Milan, et al.
Published: (2024)
by: Kuzmanovic, Milan, et al.
Published: (2024)
Overlap-Adaptive Regularization for Conditional Average Treatment Effect Estimation
by: Melnychuk, Valentyn, et al.
Published: (2025)
by: Melnychuk, Valentyn, et al.
Published: (2025)
LLM-Driven Treatment Effect Estimation Under Inference Time Text Confounding
by: Ma, Yuchen, et al.
Published: (2025)
by: Ma, Yuchen, et al.
Published: (2025)
Orthogonal Representation Learning for Estimating Causal Quantities
by: Melnychuk, Valentyn, et al.
Published: (2025)
by: Melnychuk, Valentyn, et al.
Published: (2025)
Generalized Bayes for Causal Inference
by: Javurek, Emil, et al.
Published: (2026)
by: Javurek, Emil, et al.
Published: (2026)
GDR-learners: Orthogonal Learning of Generative Models for Potential Outcomes
by: Melnychuk, Valentyn, et al.
Published: (2025)
by: Melnychuk, Valentyn, et al.
Published: (2025)
Consistent End-to-End Estimation for Counterfactual Fairness
by: Ma, Yuchen, et al.
Published: (2023)
by: Ma, Yuchen, et al.
Published: (2023)
Orthogonal Learner for Estimating Heterogeneous Long-Term Treatment Effects
by: Ma, Haorui, et al.
Published: (2026)
by: Ma, Haorui, et al.
Published: (2026)
Rank-Learner: Orthogonal Ranking of Treatment Effects
by: Arno, Henri, et al.
Published: (2026)
by: Arno, Henri, et al.
Published: (2026)
Nonparametric LLM Evaluation from Preference Data
by: Frauen, Dennis, et al.
Published: (2026)
by: Frauen, Dennis, et al.
Published: (2026)
Amortizing Causal Sensitivity Analysis via Prior Data-Fitted Networks
by: Javurek, Emil, et al.
Published: (2026)
by: Javurek, Emil, et al.
Published: (2026)
Meta-Learners for Partially-Identified Treatment Effects Across Multiple Environments
by: Schweisthal, Jonas, et al.
Published: (2024)
by: Schweisthal, Jonas, et al.
Published: (2024)
Adaptive Experimentation for Censored Survival Outcomes
by: Wang, Yuxin, et al.
Published: (2026)
by: Wang, Yuxin, et al.
Published: (2026)
Treatment Effect Estimation for Optimal Decision-Making
by: Frauen, Dennis, et al.
Published: (2025)
by: Frauen, Dennis, et al.
Published: (2025)
Improving the Generation and Evaluation of Synthetic Data for Downstream Medical Causal Inference
by: Amad, Harry, et al.
Published: (2025)
by: Amad, Harry, et al.
Published: (2025)
A Neural Framework for Generalized Causal Sensitivity Analysis
by: Frauen, Dennis, et al.
Published: (2023)
by: Frauen, Dennis, et al.
Published: (2023)
Estimating heterogeneous treatment effects with survival outcomes via a deep survival learner
by: Sun, Yuming, et al.
Published: (2026)
by: Sun, Yuming, et al.
Published: (2026)
Causal methods for LLM development and evaluation
by: Frauen, Dennis, et al.
Published: (2026)
by: Frauen, Dennis, et al.
Published: (2026)
ConfoundingSHAP: Quantifying confounding strength in causal inference
by: Brockschmidt, Marie, et al.
Published: (2026)
by: Brockschmidt, Marie, et al.
Published: (2026)
Robust estimation of heterogeneous treatment effects in randomized trials leveraging external data
by: Karlsson, Rickard, et al.
Published: (2025)
by: Karlsson, Rickard, et al.
Published: (2025)
Predicting Startup Success Using Large Language Models: A Novel In-Context Learning Approach
by: Maarouf, Abdurahman, et al.
Published: (2026)
by: Maarouf, Abdurahman, et al.
Published: (2026)
Similar Items
-
Overlap-weighted orthogonal meta-learner for treatment effect estimation over time
by: Hess, Konstantin, et al.
Published: (2025) -
IGC-Net for conditional average potential outcome estimation over time
by: Hess, Konstantin, et al.
Published: (2024) -
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) -
Bayesian Neural Controlled Differential Equations for Treatment Effect Estimation
by: Hess, Konstantin, et al.
Published: (2023)