Causality-oriented robustness: exploiting general noise interventions
Fuente:
arXiv
Saved in:
| Main Authors: | Shen, Xinwei, Bühlmann, Peter, Taeb, Armeen |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
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)
Causality-Inspired Robustness for Nonlinear Models via Representation Learning
by: Šola, Marin, et al.
Published: (2025)
by: Šola, Marin, et al.
Published: (2025)
Extremal graphical modeling with latent variables via convex optimization
by: Engelke, Sebastian, et al.
Published: (2024)
by: Engelke, Sebastian, et al.
Published: (2024)
Model Selection over Partially Ordered Sets
by: Taeb, Armeen, et al.
Published: (2023)
by: Taeb, Armeen, et al.
Published: (2023)
Integer Programming for Learning Directed Acyclic Graphs from Non-identifiable Gaussian Models
by: Xu, Tong, et al.
Published: (2024)
by: Xu, Tong, et al.
Published: (2024)
A spectral method for multi-view subspace learning using the product of projections
by: Sergazinov, Renat, et al.
Published: (2024)
by: Sergazinov, Renat, et al.
Published: (2024)
Invariant Probabilistic Prediction
by: Henzi, Alexander, et al.
Published: (2023)
by: Henzi, Alexander, et al.
Published: (2023)
Assessing the overall and partial causal well-specification of nonlinear additive noise models
by: Schultheiss, Christoph, et al.
Published: (2023)
by: Schultheiss, Christoph, et al.
Published: (2023)
Causal inference using invariant prediction: identification and confidence intervals
by: Peters, Jonas, et al.
Published: (2015)
by: Peters, Jonas, et al.
Published: (2015)
Model-oriented Graph Distances via Partially Ordered Sets
by: Taeb, Armeen, et al.
Published: (2025)
by: Taeb, Armeen, et al.
Published: (2025)
Convex Mixed-Integer Programming for Causal Additive Models with Optimization and Statistical Guarantees
by: Zhang, Xiaozhu, et al.
Published: (2025)
by: Zhang, Xiaozhu, et al.
Published: (2025)
The Causal Chambers: Real Physical Systems as a Testbed for AI Methodology
by: Gamella, Juan L., et al.
Published: (2024)
by: Gamella, Juan L., et al.
Published: (2024)
Quantifying uncertainty and stability among highly correlated predictors: a subspace perspective
by: Zhang, Xiaozhu, et al.
Published: (2025)
by: Zhang, Xiaozhu, et al.
Published: (2025)
Clustered random forests with correlated data for optimal estimation and inference under potential covariate shift
by: Young, Elliot H., et al.
Published: (2025)
by: Young, Elliot H., et al.
Published: (2025)
Distributionally Robust Learning for Multi-source Unsupervised Domain Adaptation
by: Wang, Zhenyu, et al.
Published: (2023)
by: Wang, Zhenyu, et al.
Published: (2023)
Frugal, Flexible, Faithful: Causal Data Simulation via Frengression
by: Yang, Linying, et al.
Published: (2025)
by: Yang, Linying, et al.
Published: (2025)
Extrapolation-Aware Nonparametric Statistical Inference
by: Pfister, Niklas, et al.
Published: (2024)
by: Pfister, Niklas, et al.
Published: (2024)
Causal Invariance Learning via Efficient Nonconvex Optimization
by: Wang, Zhenyu, et al.
Published: (2024)
by: Wang, Zhenyu, et al.
Published: (2024)
Causality Pursuit from Heterogeneous Environments via Neural Adversarial Invariance Learning
by: Gu, Yihong, et al.
Published: (2024)
by: Gu, Yihong, et al.
Published: (2024)
Robustness Against Weak or Invalid Instruments: Exploring Nonlinear Treatment Models with Machine Learning
by: Guo, Zijian, et al.
Published: (2022)
by: Guo, Zijian, et al.
Published: (2022)
Engression: Extrapolation through the Lens of Distributional Regression
by: Shen, Xinwei, et al.
Published: (2023)
by: Shen, Xinwei, et al.
Published: (2023)
Distributional Principal Autoencoders
by: Shen, Xinwei, et al.
Published: (2024)
by: Shen, Xinwei, et al.
Published: (2024)
Conditional Local Independence Testing for Itô processes with Applications to Dynamic Causal Discovery
by: Liu, Mingzhou, et al.
Published: (2025)
by: Liu, Mingzhou, et al.
Published: (2025)
Domain Generalization and Adaptation in Intensive Care with Anchor Regression
by: Londschien, Malte, et al.
Published: (2025)
by: Londschien, Malte, et al.
Published: (2025)
Doubly Robust Proximal Causal Learning for Continuous Treatments
by: Wu, Yong, et al.
Published: (2023)
by: Wu, Yong, et al.
Published: (2023)
Causal Discovery via Conditional Independence Testing with Proxy Variables
by: Liu, Mingzhou, et al.
Published: (2023)
by: Liu, Mingzhou, et al.
Published: (2023)
Consensus Tree Estimation with False Discovery Rate Control via Partially Ordered Sets
by: Cabrera, Maria Alejandra Valdez, et al.
Published: (2025)
by: Cabrera, Maria Alejandra Valdez, et al.
Published: (2025)
Reverse Markov Learning: Multi-Step Generative Models for Complex Distributions
by: Shen, Xinwei, et al.
Published: (2025)
by: Shen, Xinwei, et al.
Published: (2025)
Treatment Effect Estimation with Observational Network Data using Machine Learning
by: Emmenegger, Corinne, et al.
Published: (2022)
by: Emmenegger, Corinne, et al.
Published: (2022)
Exponential Lasso: robust sparse penalization under heavy-tailed noise and outliers with exponential-type loss
by: Mai, The Tien
Published: (2025)
by: Mai, The Tien
Published: (2025)
Distributional Instrumental Variable Method
by: Holovchak, Anastasiia, et al.
Published: (2025)
by: Holovchak, Anastasiia, et al.
Published: (2025)
Minimizing robust density power-based divergences for general parametric density models
by: Okuno, Akifumi
Published: (2023)
by: Okuno, Akifumi
Published: (2023)
Optimistic search: Change point estimation for large-scale data via adaptive logarithmic queries
by: Kovács, Solt, et al.
Published: (2020)
by: Kovács, Solt, et al.
Published: (2020)
Numerically robust Gaussian state estimation with singular observation noise
by: Krämer, Nicholas, et al.
Published: (2025)
by: Krämer, Nicholas, et al.
Published: (2025)
Discovering Causal Relationships using Proxy Variables under Unmeasured Confounding
by: Wu, Yong, et al.
Published: (2025)
by: Wu, Yong, et al.
Published: (2025)
Causal-learn: Causal Discovery in Python
by: Zheng, Yujia, et al.
Published: (2023)
by: Zheng, Yujia, et al.
Published: (2023)
Causality-Encoded Diffusion Models for Interventional Sampling and Edge Inference
by: Chen, Li, et al.
Published: (2026)
by: Chen, Li, et al.
Published: (2026)
Interventional Processes for Causal Uncertainty Quantification
by: Dance, Hugh, et al.
Published: (2024)
by: Dance, Hugh, et al.
Published: (2024)
Causal inference through multi-stage learning and doubly robust deep neural networks
by: Zhang, Yuqian, et al.
Published: (2024)
by: Zhang, Yuqian, et al.
Published: (2024)
Causally-Aware Unsupervised Feature Selection Learning
by: Shen, Zongxin, et al.
Published: (2024)
by: Shen, Zongxin, et al.
Published: (2024)
Similar Items
-
Characterization and Greedy Learning of Gaussian Structural Causal Models under Unknown Interventions
by: Gamella, Juan L., et al.
Published: (2022) -
Causality-Inspired Robustness for Nonlinear Models via Representation Learning
by: Šola, Marin, et al.
Published: (2025) -
Extremal graphical modeling with latent variables via convex optimization
by: Engelke, Sebastian, et al.
Published: (2024) -
Model Selection over Partially Ordered Sets
by: Taeb, Armeen, et al.
Published: (2023) -
Integer Programming for Learning Directed Acyclic Graphs from Non-identifiable Gaussian Models
by: Xu, Tong, et al.
Published: (2024)