Cover Image

Disentangled Representation Learning for Causal Inference with Instruments

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Cheng, Debo, Li, Jiuyong, Liu, Lin, Xu, Ziqi, Zhang, Weijia, Liu, Jixue, Le, Thuc Duy
Format:	Preprint
Published:	2024
Subjects:	Machine Learning Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

TSI: A Multi-View Representation Learning Approach for Time Series Forecasting
by: Gao, Wentao, et al.
Published: (2024)

Leaning Time-Varying Instruments for Identifying Causal Effects in Time-Series Data
by: Cheng, Debo, et al.
Published: (2024)

Linking Model Intervention to Causal Interpretation in Model Explanation
by: Cheng, Debo, et al.
Published: (2024)

Dependency-based Anomaly Detection: a General Framework and Comprehensive Evaluation
by: Lu, Sha, et al.
Published: (2020)

Peer Effect Estimation in the Presence of Simultaneous Feedback and Unobserved Confounders
by: Du, Xiaojing, et al.
Published: (2025)

uLEAD-TabPFN: Uncertainty-aware Dependency-based Anomaly Detection with TabPFN
by: Lu, Sha, et al.
Published: (2026)

Deconfounding Multi-Cause Latent Confounders: A Factor-Model Approach to Climate Model Bias Correction
by: Gao, Wentao, et al.
Published: (2024)

Disentangled Instrumental Variables for Causal Inference with Networked Observational Data
by: Huang, Zhirong, et al.
Published: (2026)

Learning Fair Graph Representations with Multi-view Information Bottleneck
by: Liu, Chuxun, et al.
Published: (2025)

From Noise to Precision: A Diffusion-Driven Approach to Zero-Inflated Precipitation Prediction
by: Gao, Wentao, et al.
Published: (2025)

Towards Fair Graph Representation Learning in Social Networks
by: Zhang, Guixian, et al.
Published: (2024)

Causal Learner: A Toolbox for Causal Structure and Markov Blanket Learning
by: Ling, Zhaolong, et al.
Published: (2021)

Identifying the Group to Intervene on to Maximise Effect Under Cross-Group Interference
by: Du, Xiaojing, et al.
Published: (2026)

Causal-Aware Generative Adversarial Networks with Reinforcement Learning
by: Nguyen, Tu Anh Hoang, et al.
Published: (2025)

Causal Effect Estimation using identifiable Variational AutoEncoder with Latent Confounders and Post-Treatment Variables
by: Xie, Yang, et al.
Published: (2024)

Interaction-Data-guided Conditional Instrumental Variables for Debiasing Recommender Systems
by: Huang, Zhirong, et al.
Published: (2024)

Debiased Contrastive Representation Learning for Mitigating Dual Biases in Recommender Systems
by: Huang, Zhirong, et al.
Published: (2024)

Is it Still Fair? A Comparative Evaluation of Fairness Algorithms through the Lens of Covariate Drift
by: Deho, Oscar Blessed, et al.
Published: (2024)

From Correlation to Causation: Max-Pooling-Based Multi-Instance Learning Leads to More Robust Whole Slide Image Classification
by: Liu, Xin, et al.
Published: (2024)

DeNoise: Learning Robust Graph Representations for Unsupervised Graph-Level Anomaly Detection
by: Chen, Qingfeng, et al.
Published: (2025)

Disentanglement in Difference: Directly Learning Semantically Disentangled Representations by Maximizing Inter-Factor Differences
by: Zhang, Xingshen, et al.
Published: (2025)

Disentangled Generative Graph Representation Learning
by: Hu, Xinyue, et al.
Published: (2024)

On the Representation of Pairwise Causal Background Knowledge and Its Applications in Causal Inference
by: Fang, Zhuangyan, et al.
Published: (2022)

Deep Causal Learning: Representation, Discovery and Inference
by: Deng, Zizhen, et al.
Published: (2022)

Disentangled Representations for Causal Cognition
by: Torresan, Filippo, et al.
Published: (2024)

Inductive Subgraphs as Shortcuts: Causal Disentanglement for Heterophilic Graph Learning
by: Wang, Xiangmeng, et al.
Published: (2026)

Humanoid-inspired Causal Representation Learning for Domain Generalization
by: Tao, Ze, et al.
Published: (2025)

Spectral Disentanglement and Enhancement: A Dual-domain Contrastive Framework for Representation Learning
by: Guo, Jinjin, et al.
Published: (2026)

Disentangling Policy from Offline Task Representation Learning via Adversarial Data Augmentation
by: Jia, Chengxing, et al.
Published: (2024)

Disentangled Representation Learning
by: Wang, Xin, et al.
Published: (2022)

A Closer Look at the Application of Causal Inference in Graph Representation Learning
by: Gao, Hang, et al.
Published: (2026)

Collaborative Cognitive Diagnosis with Disentangled Representation Learning for Learner Modeling
by: Gao, Weibo, et al.
Published: (2024)

Disentangling Causal Substructures for Interpretable and Generalizable Drug Synergy Prediction
by: Luo, Yi, et al.
Published: (2025)

Confounded Causal Imitation Learning with Instrumental Variables
by: Zeng, Yan, et al.
Published: (2025)

Learning Disentangled Equivariant Representation for Explicitly Controllable 3D Molecule Generation
by: Liu, Haoran, et al.
Published: (2024)

Seeking the Sufficiency and Necessity Causal Features in Multimodal Representation Learning
by: Chen, Boyu, et al.
Published: (2024)

Disentangling Multiplex Spatial-Temporal Transition Graph Representation Learning for Socially Enhanced POI Recommendation
by: Li, Jie, et al.
Published: (2025)

Disentangled and Self-Explainable Node Representation Learning
by: Piaggesi, Simone, et al.
Published: (2024)

Mitigating Propensity Bias of Large Language Models for Recommender Systems
by: Zhang, Guixian, et al.
Published: (2024)

Disentangled Double Machine Learning for Accurate Causal Effect Estimation
by: Xiang, Guodu, et al.
Published: (2026)