Characterization and Learning of Causal Graphs with Latent Confounders and Post-treatment Selection from Interventional Data
Fuente:
arXiv
Saved in:
| Main Authors: | Luo, Gongxu, Li, Loka, Chen, Guangyi, Dai, Haoyue, Zhang, Kun |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Gene Regulatory Network Inference in the Presence of Selection Bias and Latent Confounders
by: Luo, Gongxu, et al.
Published: (2025)
by: Luo, Gongxu, et al.
Published: (2025)
Causal Representation Learning from Multimodal Biomedical Observations
by: Sun, Yuewen, et al.
Published: (2024)
by: Sun, Yuewen, et al.
Published: (2024)
Federated Causal Discovery from Heterogeneous Data
by: Li, Loka, et al.
Published: (2024)
by: Li, Loka, et al.
Published: (2024)
Causal Effect Estimation using identifiable Variational AutoEncoder with Latent Confounders and Post-Treatment Variables
by: Xie, Yang, et al.
Published: (2024)
by: Xie, Yang, et al.
Published: (2024)
On the Three Demons in Causality in Finance: Time Resolution, Nonstationarity, and Latent Factors
by: Dong, Xinshuai, et al.
Published: (2023)
by: Dong, Xinshuai, et al.
Published: (2023)
When Selection Meets Intervention: Additional Complexities in Causal Discovery
by: Dai, Haoyue, et al.
Published: (2025)
by: Dai, Haoyue, et al.
Published: (2025)
Causal Effect Identification in LiNGAM Models with Latent Confounders
by: Tramontano, Daniele, et al.
Published: (2024)
by: Tramontano, Daniele, et al.
Published: (2024)
Causal Structure Representation Learning of Confounders in Latent Space for Recommendation
by: Xu, Hangtong, et al.
Published: (2023)
by: Xu, Hangtong, et al.
Published: (2023)
Towards Identifiability of Hierarchical Temporal Causal Representation Learning
by: Li, Zijian, et al.
Published: (2025)
by: Li, Zijian, et al.
Published: (2025)
Automating the Selection of Proxy Variables of Unmeasured Confounders
by: Xie, Feng, et al.
Published: (2024)
by: Xie, Feng, et al.
Published: (2024)
Regression-Based Estimation of Causal Effects in the Presence of Selection Bias and Confounding
by: Hafer, Marlies, et al.
Published: (2025)
by: Hafer, Marlies, et al.
Published: (2025)
A Recipe for Causal Graph Regression: Confounding Effects Revisited
by: Yin, Yujia, et al.
Published: (2025)
by: Yin, Yujia, et al.
Published: (2025)
Nonparametric Identification and Inference for Counterfactual Distributions with Confounding
by: Sun, Jianle, et al.
Published: (2026)
by: Sun, Jianle, et al.
Published: (2026)
Causal Inference with Categorical Unobserved Confounder via Mixture Learning
by: Saha, Aytijhya, et al.
Published: (2026)
by: Saha, Aytijhya, et al.
Published: (2026)
Identification of Causal Direction under an Arbitrary Number of Latent Confounders
by: Chen, Wei, et al.
Published: (2025)
by: Chen, Wei, et al.
Published: (2025)
Feature Matching Intervention: Leveraging Observational Data for Causal Representation Learning
by: Li, Haoze, et al.
Published: (2025)
by: Li, Haoze, et al.
Published: (2025)
Sample Efficient Bayesian Learning of Causal Graphs from Interventions
by: Zhou, Zihan, et al.
Published: (2024)
by: Zhou, Zihan, et al.
Published: (2024)
Causal Discovery of Linear Non-Gaussian Causal Models with Unobserved Confounding
by: Schkoda, Daniela, et al.
Published: (2024)
by: Schkoda, Daniela, et al.
Published: (2024)
Estimating Interventional Distributions with Uncertain Causal Graphs through Meta-Learning
by: Dhir, Anish, et al.
Published: (2025)
by: Dhir, Anish, et al.
Published: (2025)
GST-UNet: A Neural Framework for Spatiotemporal Causal Inference with Time-Varying Confounding
by: Oprescu, Miruna, et al.
Published: (2025)
by: Oprescu, Miruna, et al.
Published: (2025)
Differentiable Cyclic Causal Discovery Under Unmeasured Confounders
by: Sethuraman, Muralikrishnna G., et al.
Published: (2025)
by: Sethuraman, Muralikrishnna G., et al.
Published: (2025)
On the Recoverability of Causal Relations from Bulk Gene Expression Data
by: Luo, Gongxu, et al.
Published: (2026)
by: Luo, Gongxu, et al.
Published: (2026)
Long-term Causal Inference Under Persistent Confounding via Data Combination
by: Imbens, Guido, et al.
Published: (2022)
by: Imbens, Guido, et al.
Published: (2022)
Towards Characterizing Domain Counterfactuals For Invertible Latent Causal Models
by: Zhou, Zeyu, et al.
Published: (2023)
by: Zhou, Zeyu, et al.
Published: (2023)
Identification of Average Causal Effects in Confounded Additive Noise Models
by: Elahi, Muhammad Qasim, et al.
Published: (2024)
by: Elahi, Muhammad Qasim, et al.
Published: (2024)
Characterization and Greedy Learning of Gaussian Structural Causal Models under Unknown Interventions
by: Gamella, Juan L., et al.
Published: (2022)
by: Gamella, Juan L., et al.
Published: (2022)
Sequential Treatment Effect Estimation with Unmeasured Confounders
by: Wang, Yingrong, et al.
Published: (2025)
by: Wang, Yingrong, et al.
Published: (2025)
Comparative Study of Causal Discovery Methods for Cyclic Models with Hidden Confounders
by: Lorbeer, Boris, et al.
Published: (2024)
by: Lorbeer, Boris, et al.
Published: (2024)
Distributional Equivalence in Linear Non-Gaussian Latent-Variable Cyclic Causal Models: Characterization and Learning
by: Dai, Haoyue, et al.
Published: (2026)
by: Dai, Haoyue, et al.
Published: (2026)
Bounds and Sensitivity Analysis of the Causal Effect Under Outcome-Independent MNAR Confounding
by: Peña, Jose M.
Published: (2024)
by: Peña, Jose M.
Published: (2024)
Causality-Encoded Diffusion Models for Interventional Sampling and Edge Inference
by: Chen, Li, et al.
Published: (2026)
by: Chen, Li, et al.
Published: (2026)
Gene Regulatory Network Inference in the Presence of Dropouts: a Causal View
by: Dai, Haoyue, et al.
Published: (2024)
by: Dai, Haoyue, et al.
Published: (2024)
A Dialogue between Causal and Traditional Representation Learning: Toward Mutual Benefits in a Unified Formulation
by: Li, Yan, et al.
Published: (2026)
by: Li, Yan, et al.
Published: (2026)
Reinforcement Learning with Continuous Actions Under Unmeasured Confounding
by: Li, Yuhan, et al.
Published: (2025)
by: Li, Yuhan, et al.
Published: (2025)
Information-Theoretic Causal Bounds under Unmeasured Confounding
by: Jung, Yonghan, et al.
Published: (2026)
by: Jung, Yonghan, et al.
Published: (2026)
Causally-Aware Unsupervised Feature Selection Learning
by: Shen, Zongxin, et al.
Published: (2024)
by: Shen, Zongxin, et al.
Published: (2024)
CaRiNG: Learning Temporal Causal Representation under Non-Invertible Generation Process
by: Chen, Guangyi, et al.
Published: (2024)
by: Chen, Guangyi, et al.
Published: (2024)
Interventional Time Series Priors for Causal Foundation Models
by: Thumm, Dennis, et al.
Published: (2026)
by: Thumm, Dennis, et al.
Published: (2026)
Separating and Learning Latent Confounders to Enhancing User Preferences Modeling
by: Xu, Hangtong, et al.
Published: (2023)
by: Xu, Hangtong, et al.
Published: (2023)
Generalized Independent Noise Condition for Estimating Causal Structure with Latent Variables
by: Xie, Feng, et al.
Published: (2023)
by: Xie, Feng, et al.
Published: (2023)
Similar Items
-
Gene Regulatory Network Inference in the Presence of Selection Bias and Latent Confounders
by: Luo, Gongxu, et al.
Published: (2025) -
Causal Representation Learning from Multimodal Biomedical Observations
by: Sun, Yuewen, et al.
Published: (2024) -
Federated Causal Discovery from Heterogeneous Data
by: Li, Loka, et al.
Published: (2024) -
Causal Effect Estimation using identifiable Variational AutoEncoder with Latent Confounders and Post-Treatment Variables
by: Xie, Yang, et al.
Published: (2024) -
On the Three Demons in Causality in Finance: Time Resolution, Nonstationarity, and Latent Factors
by: Dong, Xinshuai, et al.
Published: (2023)