Cover Image

Learning Structural Causal Models through Deep Generative Models: Methods, Guarantees, and Challenges

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Poinsot, Audrey, Leite, Alessandro, Chesneau, Nicolas, Sébag, Michèle, Schoenauer, Marc
Format:	Preprint
Published:	2024
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

CausalProfiler: Generating Synthetic Benchmarks for Rigorous and Transparent Evaluation of Causal Machine Learning
by: Panayiotou, Panayiotis, et al.
Published: (2025)

Position: Causal Machine Learning Requires Rigorous Synthetic Experiments for Broader Adoption
by: Poinsot, Audrey, et al.
Published: (2025)

Mixture of Experts in Image Classification: What's the Sweet Spot?
by: Videau, Mathurin, et al.
Published: (2024)

Asymmetrical Latent Representation for Individual Treatment Effect Modeling
by: Lacombe, Armand, et al.
Published: (2025)

TFM-Retouche: A Lightweight Input-Space Adapter for Tabular Foundation Models
by: Nguyen, Duong, et al.
Published: (2026)

DCILP: A Distributed Approach for Large-Scale Causal Structure Learning
by: Dong, Shuyu, et al.
Published: (2024)

Multi-Level GNN Preconditioner for Solving Large Scale Problems
by: Nastorg, Matthieu, et al.
Published: (2024)

Learning Large Causal Structures from Inverse Covariance Matrix via Sparse Matrix Decomposition
by: Dong, Shuyu, et al.
Published: (2022)

Provably Safeguarding a Classifier from OOD and Adversarial Samples: an Extreme Value Theory Approach
by: Atienza, Nicolas, et al.
Published: (2025)

Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training
by: Labiad, Ismail, et al.
Published: (2025)

Evolutionary Retrofitting
by: Videau, Mathurin, et al.
Published: (2024)

An Implicit GNN Solver for Poisson-like problems
by: Nastorg, Matthieu, et al.
Published: (2023)

Interventional Causal Structure Discovery over Graphical Models with Convergence and Optimality Guarantees
by: Chengbo, Qiu, et al.
Published: (2024)

Self-AMPLIFY: Improving Small Language Models with Self Post Hoc Explanations
by: Bhan, Milan, et al.
Published: (2024)

Causal Concept Graph Models: Beyond Causal Opacity in Deep Learning
by: Dominici, Gabriele, et al.
Published: (2024)

Learning Guarantee of Reward Modeling Using Deep Neural Networks
by: Luo, Yuanhang, et al.
Published: (2025)

Foundation models for time series forecasting: Application in conformal prediction
by: Achour, Sami, et al.
Published: (2025)

Conformal Approach To Gaussian Process Surrogate Evaluation With Coverage Guarantees
by: Jaber, Edgar, et al.
Published: (2024)

Convergence Guarantees for the DeepWalk Embedding on Block Models
by: Harker, Christopher, et al.
Published: (2024)

Generalization Bounds for Causal Regression: Insights, Guarantees and Sensitivity Analysis
by: Csillag, Daniel, et al.
Published: (2024)

Towards Empowerment Gain through Causal Structure Learning in Model-Based RL
by: Cao, Hongye, et al.
Published: (2025)

Modular Learning of Deep Causal Generative Models for High-dimensional Causal Inference
by: Rahman, Md Musfiqur, et al.
Published: (2024)

Learning Causal Abstractions of Linear Structural Causal Models
by: Massidda, Riccardo, et al.
Published: (2024)

Semi-Supervised Learning for Deep Causal Generative Models
by: Ibrahim, Yasin, et al.
Published: (2024)

Towards Interpretable Deep Generative Models via Causal Representation Learning
by: Moran, Gemma E., et al.
Published: (2025)

Standardizing Structural Causal Models
by: Ormaniec, Weronika, et al.
Published: (2024)

Structural Causal Bottleneck Models
by: Bing, Simon, et al.
Published: (2026)

Learning and Transferring Physical Models through Derivatives
by: Trenta, Alessandro, et al.
Published: (2025)

Semi-Supervised Learning under General Causal Models
by: Moore, Archer, et al.
Published: (2025)

Causal Bandits with General Causal Models and Interventions
by: Yan, Zirui, et al.
Published: (2024)

Generalization Error Guaranteed Auto-Encoder-Based Nonlinear Model Reduction for Operator Learning
by: Liu, Hao, et al.
Published: (2024)

ML4PhySim : Machine Learning for Physical Simulations Challenge (The airfoil design)
by: Yagoubi, Mouadh, et al.
Published: (2024)

Aquatic Navigation: A Challenging Benchmark for Deep Reinforcement Learning
by: Corsi, Davide, et al.
Published: (2024)

Federated Causal Inference in Healthcare: Methods, Challenges, and Applications
by: Li, Haoyang, et al.
Published: (2025)

Emerging Synergies in Causality and Deep Generative Models: A Survey
by: Zhou, Guanglin, et al.
Published: (2023)

Causal-INSIGHT: Probing Temporal Models to Extract Causal Structure
by: Redden, Benjamin, et al.
Published: (2026)

Deep Autoregressive Models as Causal Inference Engines
by: Im, Daniel Jiwoong, et al.
Published: (2024)

Sparse Additive Model Pruning for Order-Based Causal Structure Learning
by: Kanamori, Kentaro, et al.
Published: (2026)

Higher-Order Causal Structure Learning with Additive Models
by: Enouen, James, et al.
Published: (2025)

Learning Structural Causal Models from Ordering: Identifiable Flow Models
by: Le, Minh Khoa, et al.
Published: (2024)