Copertina

LLM-Driven Treatment Effect Estimation Under Inference Time Text Confounding

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Ma, Yuchen, Frauen, Dennis, Schweisthal, Jonas, Feuerriegel, Stefan
Natura:	Preprint
Pubblicazione:	2025
Soggetti:	Machine Learning
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Generalized Bayes for Causal Inference
di: Javurek, Emil, et al.
Pubblicazione: (2026)

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

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

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

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

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

Targeted Synthetic Control Method
di: Wang, Yuxin, et al.
Pubblicazione: (2026)

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

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

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

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

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

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

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

Conditional Average Treatment Effect Estimation Under Hidden Confounders
di: Aloui, Ahmed, et al.
Pubblicazione: (2025)

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

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

Sequential Treatment Effect Estimation with Unmeasured Confounders
di: Wang, Yingrong, et al.
Pubblicazione: (2025)