Copertina

Consistent End-to-End Estimation for Counterfactual Fairness

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Ma, Yuchen, Melnychuk, Valentyn, Frauen, Dennis, Feuerriegel, Stefan
Natura:	Preprint
Pubblicazione:	2023
Soggetti:	Machine Learning Computers and Society
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

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

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)

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

End-to-End Optimization and Learning of Fair Court Schedules
di: Dinh, My H, et al.
Pubblicazione: (2024)

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

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

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

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

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

Algorithmic Fairness in AI Surrogates for End-of-Life Decision-Making
di: Ahmad, Muhammad Aurangzeb
Pubblicazione: (2025)

Do Fair Models Reason Fairly? Counterfactual Explanation Consistency for Procedural Fairness in Credit Decisions
di: Popoola, Gideon, et al.
Pubblicazione: (2026)

Counterfactual Fairness Is Not Demographic Parity, and Other Observations
di: Silva, Ricardo
Pubblicazione: (2024)

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

Counterfactual Fairness with Graph Uncertainty
di: Valério, Davi, et al.
Pubblicazione: (2026)

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

FairPFN: Transformers Can do Counterfactual Fairness
di: Robertson, Jake, et al.
Pubblicazione: (2024)

Counterfactual Fairness Evaluation of Machine Learning Models on Educational Datasets
di: Kim, Woojin, et al.
Pubblicazione: (2025)

Counterfactual Situation Testing: Uncovering Discrimination under Fairness given the Difference
di: Alvarez, Jose M., et al.
Pubblicazione: (2023)

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)

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

Procedural Fairness via Group Counterfactual Explanation
di: Popoola, Gideon, et al.
Pubblicazione: (2026)

Counterfactually Fair Reinforcement Learning via Sequential Data Preprocessing
di: Wang, Jitao, et al.
Pubblicazione: (2025)

A New Paradigm for Counterfactual Reasoning in Fairness and Recourse
di: Bynum, Lucius E. J., et al.
Pubblicazione: (2024)

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