:: Library Catalog

Image de couverture de livre

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Tan, Pengcheng, Chen, Jiang, Du, Dehui
Format:	Preprint
Publié:	2026
Sujets:	Machine Learning Methodology
Accès en ligne:	https://arxiv.org/abs/2605.04413
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

On the representation and learning of monotone triangular transport maps
par: Baptista, Ricardo, et autres
Publié: (2020)

Online monotone density estimation and log-optimal calibration
par: Hore, Rohan, et autres
Publié: (2026)

Scalable branch-and-bound model selection with non-monotonic criteria including AIC, BIC and Mallows's $\mathit{C_p}$
par: Vanhoefer, Jakob, et autres
Publié: (2025)

Parameter identification in linear non-Gaussian causal models under general confounding
par: Tramontano, Daniele, et autres
Publié: (2024)

Semiparametric principal stratification analysis beyond monotonicity
par: Tong, Jiaqi, et autres
Publié: (2025)

Controlling for discrete unmeasured confounding in nonlinear causal models
par: Burauel, Patrick, et autres
Publié: (2024)

Exogenous Isomorphism for Counterfactual Identifiability
par: Chen, Yikang, et autres
Publié: (2025)

DiffusionCounterfactuals: Inferring High-dimensional Counterfactuals with Guidance of Causal Representations
par: Zhu, Jiageng, et autres
Publié: (2024)

Deciphering interventional dynamical causality from non-intervention complex systems
par: Shi, Jifan, et autres
Publié: (2024)

CFLight: Enhancing Safety with Traffic Signal Control through Counterfactual Learning
par: Li, Mingyuan, et autres
Publié: (2025)

Counterfactual Generative Models for Time-Varying Treatments
par: Wu, Shenghao, et autres
Publié: (2023)

Semiparametric Counterfactual Regression
par: Kim, Kwangho
Publié: (2025)

Dynamical causality under invisible confounders
par: Yan, Jinling, et autres
Publié: (2024)

C-XGBoost: A tree boosting model for causal effect estimation
par: Kiriakidou, Niki, et autres
Publié: (2024)

DoWhy-GCM: An extension of DoWhy for causal inference in graphical causal models
par: Blöbaum, Patrick, et autres
Publié: (2022)

Assessing the overall and partial causal well-specification of nonlinear additive noise models
par: Schultheiss, Christoph, et autres
Publié: (2023)

Watermarking Counterfactual Explanations
par: Guo, Hangzhi, et autres
Publié: (2024)

Generalized Encouragement-Based Instrumental Variables for Counterfactual Regression
par: Wu, Anpeng, et autres
Publié: (2024)

Scalable Counterfactual Risk Estimation for Rare Events in Longitudinal Data
par: Yin, Xiaohui, et autres
Publié: (2026)

Learning linear acyclic causal model including Gaussian noise using ancestral relationships
par: Cai, Ming, et autres
Publié: (2024)

Collaborative causal inference on distributed data
par: Kawamata, Yuji, et autres
Publié: (2022)

Nonparametric Identification and Inference for Counterfactual Distributions with Confounding
par: Sun, Jianle, et autres
Publié: (2026)

DoubleGen: Debiased Generative Modeling of Counterfactuals
par: Luedtke, Alex, et autres
Publié: (2025)

Causal and Counterfactual Views of Missing Data Models
par: Nabi, Razieh, et autres
Publié: (2022)

The causal structure of galactic astrophysics
par: Desmond, Harry, et autres
Publié: (2025)

Counterfactual Cocycles: A Framework for Robust and Coherent Counterfactual Transports
par: Dance, Hugh, et autres
Publié: (2024)

Targeting relative risk heterogeneity with causal forests
par: Shirvaikar, Vik, et autres
Publié: (2023)

Separation-based distance measures for causal graphs
par: Wahl, Jonas, et autres
Publié: (2024)

Advancing Counterfactual Inference through Nonlinear Quantile Regression
par: Xie, Shaoan, et autres
Publié: (2023)

Assessing Electricity Service Unfairness with Transfer Counterfactual Learning
par: Wei, Song, et autres
Publié: (2023)

Causal Contrastive Learning for Counterfactual Regression Over Time
par: Bouchattaoui, Mouad El, et autres
Publié: (2024)

Overview and practical recommendations on using Shapley Values for identifying predictive biomarkers via CATE modeling
par: Svensson, David, et autres
Publié: (2025)

Assumption violations in causal discovery and the robustness of score matching
par: Montagna, Francesco, et autres
Publié: (2023)

Geometry Adaptive Counterfactual Distribution Learning with Diffusion-Guided Smoothing
par: Kim, Kwangho
Publié: (2026)

DR-VIDAL -- Doubly Robust Variational Information-theoretic Deep Adversarial Learning for Counterfactual Prediction and Treatment Effect Estimation on Real World Data
par: Ghosh, Shantanu, et autres
Publié: (2023)

Collective Counterfactual Explanations: Balancing Individual Goals and Collective Dynamics
par: Ehyaei, Ahmad-Reza, et autres
Publié: (2024)

Response to Discussions of "Causal and Counterfactual Views of Missing Data Models"
par: Nabi, Razieh, et autres
Publié: (2025)

Modeling Causal Mechanisms with Diffusion Models for Interventional and Counterfactual Queries
par: Chao, Patrick, et autres
Publié: (2023)

Sequential Conditional Transport on Probabilistic Graphs for Interpretable Counterfactual Fairness
par: Machado, Agathe Fernandes, et autres
Publié: (2024)

Towards Characterizing Domain Counterfactuals For Invertible Latent Causal Models
par: Zhou, Zeyu, et autres
Publié: (2023)