Higher-Order Causal Structure Learning with Additive Models
Fuente:
arXiv
Saved in:
| Main Authors: | Enouen, James, Zheng, Yujia, Ng, Ignavier, Liu, Yan, Zhang, Kun |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Causal Representation Learning from Multiple Distributions: A General Setting
by: Zhang, Kun, et al.
Published: (2024)
by: Zhang, Kun, et al.
Published: (2024)
Diverse Dictionary Learning
by: Zheng, Yujia, et al.
Published: (2026)
by: Zheng, Yujia, et al.
Published: (2026)
Boosting Causal Additive Models
by: Kertel, Maximilian, et al.
Published: (2024)
by: Kertel, Maximilian, et al.
Published: (2024)
Nonparametric Factor Analysis and Beyond
by: Zheng, Yujia, et al.
Published: (2025)
by: Zheng, Yujia, et al.
Published: (2025)
Higher-Order Regularization Learning on Hypergraphs
by: Weihs, Adrien, et al.
Published: (2025)
by: Weihs, Adrien, et al.
Published: (2025)
InstaSHAP: Interpretable Additive Models Explain Shapley Values Instantly
by: Enouen, James, et al.
Published: (2025)
by: Enouen, James, et al.
Published: (2025)
On the Identifiability of Nonlinear ICA: Sparsity and Beyond
by: Zheng, Yujia, et al.
Published: (2022)
by: Zheng, Yujia, et al.
Published: (2022)
Local Causal Discovery with Linear non-Gaussian Cyclic Models
by: Dai, Haoyue, et al.
Published: (2024)
by: Dai, Haoyue, et al.
Published: (2024)
Local Learning for Covariate Selection in Nonparametric Causal Effect Estimation with Latent Variables
by: Li, Zheng, et al.
Published: (2024)
by: Li, Zheng, et al.
Published: (2024)
Detecting and Identifying Selection Structure in Sequential Data
by: Zheng, Yujia, et al.
Published: (2024)
by: Zheng, Yujia, et al.
Published: (2024)
Fast and Efficient Parallel Sampling Using Higher Order Langevin Dynamics
by: Mahajan, Jaideep, et al.
Published: (2025)
by: Mahajan, Jaideep, et al.
Published: (2025)
Dynamic Structural Causal Models
by: Boeken, Philip, et al.
Published: (2024)
by: Boeken, Philip, et al.
Published: (2024)
On the Identifiability of Sparse ICA without Assuming Non-Gaussianity
by: Ng, Ignavier, et al.
Published: (2024)
by: Ng, Ignavier, et al.
Published: (2024)
Differentiable Causal Discovery For Latent Hierarchical Causal Models
by: Prashant, Parjanya, et al.
Published: (2024)
by: Prashant, Parjanya, et al.
Published: (2024)
Faster Diffusion Models via Higher-Order Approximation
by: Li, Gen, et al.
Published: (2025)
by: Li, Gen, et al.
Published: (2025)
Identifying Drift, Diffusion, and Causal Structure from Temporal Snapshots
by: Guan, Vincent, et al.
Published: (2024)
by: Guan, Vincent, et al.
Published: (2024)
Structure Learning with Continuous Optimization: A Sober Look and Beyond
by: Ng, Ignavier, et al.
Published: (2023)
by: Ng, Ignavier, et al.
Published: (2023)
P-Tensors: a General Formalism for Constructing Higher Order Message Passing Networks
by: Hands, Andrew, et al.
Published: (2023)
by: Hands, Andrew, et al.
Published: (2023)
Differentiable Structure Learning and Causal Discovery for General Binary Data
by: Deng, Chang, et al.
Published: (2025)
by: Deng, Chang, et al.
Published: (2025)
Online Quantile Regression for Nonparametric Additive Models
by: Zhan, Haoran
Published: (2026)
by: Zhan, Haoran
Published: (2026)
Trek-Based Parameter Identification for Linear Causal Models With Arbitrarily Structured Latent Variables
by: Sturma, Nils, et al.
Published: (2025)
by: Sturma, Nils, et al.
Published: (2025)
Statistical Inference and Learning for Shapley Additive Explanations (SHAP)
by: Whitehouse, Justin, et al.
Published: (2026)
by: Whitehouse, Justin, et al.
Published: (2026)
Kernel Two-Sample Tests in High Dimension: Interplay Between Moment Discrepancy and Dimension-and-Sample Orders
by: Yan, Jian, et al.
Published: (2021)
by: Yan, Jian, et al.
Published: (2021)
Causal de Finetti: On the Identification of Invariant Causal Structure in Exchangeable Data
by: Guo, Siyuan, et al.
Published: (2022)
by: Guo, Siyuan, et al.
Published: (2022)
Stochastic Deep Learning: A Probabilistic Framework for Modeling Uncertainty in Structured Temporal Data
by: Rice, James
Published: (2026)
by: Rice, James
Published: (2026)
Transfer Learning for Causal Effect Estimation
by: Wei, Song, et al.
Published: (2023)
by: Wei, Song, et al.
Published: (2023)
Functional Linear Non-Gaussian Acyclic Model for Causal Discovery
by: Yang, Tian-Le, et al.
Published: (2024)
by: Yang, Tian-Le, et al.
Published: (2024)
Degree Heterogeneity in Higher-Order Networks: Inference in the Hypergraph $\boldsymbolβ$-Model
by: Nandy, Sagnik, et al.
Published: (2023)
by: Nandy, Sagnik, et al.
Published: (2023)
Minimax Signal Detection in Sparse Additive Models
by: Kotekal, Subhodh, et al.
Published: (2023)
by: Kotekal, Subhodh, et al.
Published: (2023)
An introduction to Causal Modelling
by: Baishya, Gauranga Kumar
Published: (2025)
by: Baishya, Gauranga Kumar
Published: (2025)
An Upper Confidence Bound Approach to Estimating the Maximum Mean
by: Kun, Zhang, et al.
Published: (2024)
by: Kun, Zhang, et al.
Published: (2024)
Foundations of Structural Causal Models with Latent Selection
by: Chen, Leihao, et al.
Published: (2024)
by: Chen, Leihao, et al.
Published: (2024)
Causal Modeling in Multi-Context Systems: Distinguishing Multiple Context-Specific Causal Graphs which Account for Observational Support
by: Rabel, Martin, et al.
Published: (2024)
by: Rabel, Martin, et al.
Published: (2024)
When Does Model Collapse Occur in Structured Interactive Learning?
by: Wu, Yuchen, et al.
Published: (2026)
by: Wu, Yuchen, et al.
Published: (2026)
Generator Identification for Linear SDEs with Additive and Multiplicative Noise
by: Wang, Yuanyuan, et al.
Published: (2023)
by: Wang, Yuanyuan, et al.
Published: (2023)
Generalized Bayesian Additive Regression Trees: Theory and Software
by: Saha, Enakshi
Published: (2023)
by: Saha, Enakshi
Published: (2023)
Learning Interpretable Concepts: Unifying Causal Representation Learning and Foundation Models
by: Rajendran, Goutham, et al.
Published: (2024)
by: Rajendran, Goutham, et al.
Published: (2024)
Sparse Tucker Decomposition and Graph Regularization for High-Dimensional Time Series Forecasting
by: Xia, Sijia, et al.
Published: (2026)
by: Xia, Sijia, et al.
Published: (2026)
A General Representation-Based Approach to Multi-Source Domain Adaptation
by: Ng, Ignavier, et al.
Published: (2026)
by: Ng, Ignavier, et al.
Published: (2026)
Estimating Higher-Order Mixed Memberships via the $\ell_{2,\infty}$ Tensor Perturbation Bound
by: Agterberg, Joshua, et al.
Published: (2022)
by: Agterberg, Joshua, et al.
Published: (2022)
Similar Items
-
Causal Representation Learning from Multiple Distributions: A General Setting
by: Zhang, Kun, et al.
Published: (2024) -
Diverse Dictionary Learning
by: Zheng, Yujia, et al.
Published: (2026) -
Boosting Causal Additive Models
by: Kertel, Maximilian, et al.
Published: (2024) -
Nonparametric Factor Analysis and Beyond
by: Zheng, Yujia, et al.
Published: (2025) -
Higher-Order Regularization Learning on Hypergraphs
by: Weihs, Adrien, et al.
Published: (2025)