:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Yvernes, Clément, Assaad, Charles K., Devijver, Emilie, Gaussier, Eric
Format:	Preprint
Published:	2025
Subjects:	Statistics Theory Artificial Intelligence
Online Access:	https://arxiv.org/abs/2506.14862
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Identifiability of total effects from abstractions of time series causal graphs
by: Assaad, Charles K., et al.
Published: (2023)

Complete Characterization for Adjustment in Summary Causal Graphs of Time Series
by: Yvernes, Clément, et al.
Published: (2025)

Identifiability in Causal Abstractions: A Hierarchy of Criteria
by: Yvernes, Clément, et al.
Published: (2025)

Relaxing partition admissibility in Cluster-DAGs: a causal calculus with arbitrary variable clustering
by: Yvernes, Clément, et al.
Published: (2025)

Note on the identification of total effect in Cluster-DAGs with cycles
by: Yvernes, Clément
Published: (2025)

On the Fly Detection of Root Causes from Observed Data with Application to IT Systems
by: Zan, Lei, et al.
Published: (2024)

Causal Discovery from Time Series with Hybrids of Constraint-Based and Noise-Based Algorithms
by: Bystrova, Daria, et al.
Published: (2023)

Towards identifiability of micro total effects in summary causal graphs with latent confounding: extension of the front-door criterion
by: Assaad, Charles K.
Published: (2024)

SCMD: A Kernel-Based Distance for Structural Causal Models to Quantify Transferability Across Environments
by: Goff, Théotime Le, et al.
Published: (2025)

Stable network inference in high-dimensional graphical model using single-linkage
by: Devijver, Emilie, et al.
Published: (2024)

A fine-grained look at causal effects in causal spaces
by: Park, Junhyung, et al.
Published: (2025)

What is causal about causal models and representations?
by: Jørgensen, Frederik Hytting, et al.
Published: (2025)

About the Cost of Central Privacy in Density Estimation
by: Lalanne, Clément, et al.
Published: (2023)

Universal time-series forecasting with mixture predictors
by: Ryabko, Daniil
Published: (2020)

Beyond identifiability: Learning causal representations with few environments and finite samples
by: Lee, Inbeom, et al.
Published: (2026)

Forecasting time series with constraints
by: Doumèche, Nathan, et al.
Published: (2025)

Causal reasoning in difference graphs
by: Assaad, Charles K.
Published: (2024)

Learning with Differentially Private (Sliced) Wasserstein Gradients
by: Rodríguez-Vítores, David, et al.
Published: (2025)

Identifiability of Potentially Degenerate Gaussian Mixture Models With Piecewise Affine Mixing
by: Xu, Danru, et al.
Published: (2026)

Identifying causal effects in maximally oriented partially directed acyclic graphs
by: Perković, Emilija
Published: (2019)

Identifiability of Direct Effects from Summary Causal Graphs
by: Ferreira, Simon, et al.
Published: (2023)

Identifying Macro Causal Effects in a C-DMG over ADMGs
by: Ferreira, Simon, et al.
Published: (2025)

Identifying Macro Causal Effects in C-DMGs over DMGs
by: Ferreira, Simon, et al.
Published: (2025)

Modèles de Fondation et Ajustement : Vers une Nouvelle Génération de Modèles pour la Prévision des Séries Temporelles
by: Laglil, Morad, et al.
Published: (2025)

Diffusion Posterior Sampling is Computationally Intractable
by: Gupta, Shivam, et al.
Published: (2024)

Identifying Macro Conditional Independencies and Macro Total Effects in Summary Causal Graphs with Latent Confounding
by: Ferreira, Simon, et al.
Published: (2024)

Quantitative causality, causality-guided scientific discovery, and causal machine learning
by: Liang, X. San, et al.
Published: (2024)

Canonical Representations of Markovian Structural Causal Models: A Framework for Counterfactual Reasoning
by: de Lara, Lucas
Published: (2025)

Statistical Inference for Misspecified Contextual Bandits
by: Guo, Yongyi, et al.
Published: (2025)

Distribution-consistency Structural Causal Models
by: Gong, Heyang, et al.
Published: (2024)

A Measure-Theoretic Axiomatisation of Causality
by: Park, Junhyung, et al.
Published: (2023)

Conditional Performance Guarantee for Large Reasoning Models
by: Huang, Jianguo, et al.
Published: (2026)

Stochastic Direct Search Method for Blind Resource Allocation
by: Achddou, Juliette, et al.
Published: (2022)

Accelerated Online Risk-Averse Policy Evaluation in POMDPs with Theoretical Guarantees and Novel CVaR Bounds
by: Pariente, Yaacov, et al.
Published: (2026)

Retraining as Approximate Bayesian Inference
by: Katz, Harrison
Published: (2026)

Physics-informed machine learning as a kernel method
by: Doumèche, Nathan, et al.
Published: (2024)

Identifying All ε-Best Arms in (Misspecified) Linear Bandits
by: Li, Zhekai, et al.
Published: (2025)

Transport f divergences
by: Li, Wuchen
Published: (2025)

The Geometry of Knowing: From Possibilistic Ignorance to Probabilistic Certainty -- A Measure-Theoretic Framework for Epistemic Convergence
by: Jah, Moriba Kemessia
Published: (2026)

The CriticalSet problem: Identifying Critical Contributors in Bipartite Dependency Networks
by: Piccolo, Sebastiano A., et al.
Published: (2026)