Cover Image

Distinguishability of causal structures under latent confounding and selection

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Carey, Ryan, Ansanelli, Marina Maciel, Wolfe, Elie, Evans, Robin J.
Format:	Preprint
Published:	2025
Subjects:	Statistics Theory Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Classifying Causal Structures: Ascertaining when Classical Correlations are Constrained by Inequalities
by: Khanna, Shashaank, et al.
Published: (2023)

Everything that can be learned about a causal structure with latent variables by observational and interventional probing schemes
by: Ansanelli, Marina Maciel, et al.
Published: (2024)

The Observational Partial Order of Causal Structures with Latent Variables
by: Ansanelli, Marina Maciel, et al.
Published: (2025)

On the physics of nested Markov models: a generalized probabilistic theory perspective
by: Zhang, Xingjian, et al.
Published: (2024)

Constraint-based causal discovery with tiered background knowledge and latent variables in single or overlapping datasets
by: Bang, Christine W., et al.
Published: (2025)

Generalization of LiNGAM that allows confounding
by: Suzuki, Joe, et al.
Published: (2024)

A fast score-based search algorithm for maximal ancestral graphs using entropy
by: Hu, Zhongyi, et al.
Published: (2024)

Incorporating structural uncertainty in causal decision making
by: Kaptein, Maurits
Published: (2025)

Model-based causal feature selection for general response types
by: Kook, Lucas, et al.
Published: (2023)

Addressing pitfalls in implicit unobserved confounding synthesis using explicit block hierarchical ancestral sampling
by: Sun, Xudong, et al.
Published: (2025)

The resource theory of causal influence and knowledge of causal influence
by: Ansanelli, Marina Maciel, et al.
Published: (2025)

Substitute adjustment via recovery of latent variables
by: Adams, Jeffrey, et al.
Published: (2024)

Multi-modal contrastive learning adapts to intrinsic dimensions of shared latent variables
by: Gui, Yu, et al.
Published: (2025)

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

When can weak latent factors be statistically inferred?
by: Fan, Jianqing, et al.
Published: (2024)

Source-Optimal Training is Transfer-Suboptimal
by: Hedges, C. Evans
Published: (2025)

Sliding down the stairs: how correlated latent variables accelerate learning with neural networks
by: Bardone, Lorenzo, et al.
Published: (2024)

Consistent estimation of generative model representations in the data kernel perspective space
by: Acharyya, Aranyak, et al.
Published: (2024)

Extremal graphical modeling with latent variables via convex optimization
by: Engelke, Sebastian, 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)

Inverting estimating equations for causal inference on quantiles
by: Cheng, Chao, et al.
Published: (2024)

Closing the problem of which causal structures of up to six total nodes have a classical-quantum gap
by: Khanna, Shashaank, et al.
Published: (2025)

Generalized coarsened confounding for causal effects: a large-sample framework
by: Ghosh, Debashis, et al.
Published: (2025)

Probably approximately correct high-dimensional causal effect estimation given a valid adjustment set
by: Choo, Davin, et al.
Published: (2024)

Dimension-free error estimate for diffusion model and optimal scheduling
by: de Bortoli, Valentin, et al.
Published: (2025)

Failures and Successes of Cross-Validation for Early-Stopped Gradient Descent
by: Patil, Pratik, et al.
Published: (2024)

Symmetric observations without symmetric causal explanations
by: William, Christian, et al.
Published: (2025)

How well behaved is finite dimensional Diffusion Maps?
by: Bo, Wenyu, et al.
Published: (2024)

Minimax Linear Regression under the Quantile Risk
by: Hanchi, Ayoub El, et al.
Published: (2024)

Online selective conformal inference: adaptive scores, convergence rate and optimality
by: Humbert, Pierre, et al.
Published: (2025)

Online and Offline Robust Multivariate Linear Regression
by: Godichon-Baggioni, Antoine, et al.
Published: (2024)

Optimizing the Induced Correlation in Omnibus Joint Graph Embeddings
by: Pantazis, Konstantinos, et al.
Published: (2024)

Optimal Ridge Regularization for Out-of-Distribution Prediction
by: Patil, Pratik, et al.
Published: (2024)

Revisiting Optimism and Model Complexity in the Wake of Overparameterized Machine Learning
by: Patil, Pratik, et al.
Published: (2024)

Adaptive Off-Policy Inference for M-Estimators Under Model Misspecification
by: Leiner, James, et al.
Published: (2025)

Spatial confounding under infill asymptotics
by: Bolin, David, et al.
Published: (2024)

On Uncertainty Calibration for Equivariant Functions
by: Berman, Edward, et al.
Published: (2025)

Multivariate Trend Filtering for Lattice Data
by: Sadhanala, Veeranjaneyulu, et al.
Published: (2021)

Cyclic quantum causal modelling with a graph separation theorem
by: Ferradini, Carla, et al.
Published: (2025)

Statistical Inference under Performativity
by: Li, Xiang, et al.
Published: (2025)