Bootstrap aggregation and confidence measures to improve time series causal discovery
Fuente:
arXiv
Saved in:
| Main Authors: | Debeire, Kevin, Runge, Jakob, Gerhardus, Andreas, Eyring, Veronika |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Causal discovery on vector-valued variables and consistency-guided aggregation
by: Ninad, Urmi, et al.
Published: (2025)
by: Ninad, Urmi, et al.
Published: (2025)
Using Time Structure to Estimate Causal Effects
by: Hochsprung, Tom, et al.
Published: (2025)
by: Hochsprung, Tom, et al.
Published: (2025)
Separation-based distance measures for causal graphs
by: Wahl, Jonas, et al.
Published: (2024)
by: Wahl, Jonas, et al.
Published: (2024)
Assumption violations in causal discovery and the robustness of score matching
by: Montagna, Francesco, et al.
Published: (2023)
by: Montagna, Francesco, et al.
Published: (2023)
Causal Inference on Process Graphs, Part I: The Structural Equation Process Representation
by: Reiter, Nicolas-Domenic, et al.
Published: (2023)
by: Reiter, Nicolas-Domenic, et al.
Published: (2023)
Causal Inference on Process Graphs, Part II: Causal Structure and Effect Identification
by: Reiter, Nicolas-Domenic, et al.
Published: (2024)
by: Reiter, Nicolas-Domenic, et al.
Published: (2024)
Causal discovery with endogenous context variables
by: Günther, Wiebke, et al.
Published: (2024)
by: Günther, Wiebke, et al.
Published: (2024)
Cross-validating causal discovery via Leave-One-Variable-Out
by: Schkoda, Daniela, et al.
Published: (2024)
by: Schkoda, Daniela, et al.
Published: (2024)
IncomeSCM: From tabular data set to time-series simulator and causal estimation benchmark
by: Johansson, Fredrik D.
Published: (2024)
by: Johansson, Fredrik D.
Published: (2024)
Sanity Checking Causal Representation Learning on a Simple Real-World System
by: Gamella, Juan L., et al.
Published: (2025)
by: Gamella, Juan L., et al.
Published: (2025)
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)
Physics-Constrained Adaptive Flow Matching for Climate Downscaling
by: Debeire, Kevin, et al.
Published: (2026)
by: Debeire, Kevin, et al.
Published: (2026)
Identifying Linearly-Mixed Causal Representations from Multi-Node Interventions
by: Bing, Simon, et al.
Published: (2023)
by: Bing, Simon, et al.
Published: (2023)
Score matching through the roof: linear, nonlinear, and latent variables causal discovery
by: Montagna, Francesco, et al.
Published: (2024)
by: Montagna, Francesco, et al.
Published: (2024)
Are you doing better than random guessing? A call for using negative controls when evaluating causal discovery algorithms
by: Petersen, Anne Helby
Published: (2024)
by: Petersen, Anne Helby
Published: (2024)
Quantum-enhanced causal discovery for a small number of samples
by: Terada, Yu, et al.
Published: (2025)
by: Terada, Yu, et al.
Published: (2025)
Causal inference using invariant prediction: identification and confidence intervals
by: Peters, Jonas, et al.
Published: (2015)
by: Peters, Jonas, et al.
Published: (2015)
A Note on the Prediction-Powered Bootstrap
by: Zrnic, Tijana
Published: (2024)
by: Zrnic, Tijana
Published: (2024)
An Online Bootstrap for Time Series
by: Palm, Nicolai, et al.
Published: (2023)
by: Palm, Nicolai, et al.
Published: (2023)
A Bootstrap-based Method for Testing Network Similarity
by: Bhadra, Somnath, et al.
Published: (2019)
by: Bhadra, Somnath, et al.
Published: (2019)
Geometric-Based Pruning Rules For Change Point Detection in Multiple Independent Time Series
by: Pishchagina, Liudmila, et al.
Published: (2023)
by: Pishchagina, Liudmila, et al.
Published: (2023)
Identifying Direct Causal Effects in Latent Factor Models by Accounting for Unidentified Parents
by: Hochsprung, Tom, et al.
Published: (2026)
by: Hochsprung, Tom, et al.
Published: (2026)
Conformal link prediction for false discovery rate control
by: Marandon, Ariane
Published: (2023)
by: Marandon, Ariane
Published: (2023)
Selecting time-series hyperparameters with the artificial jackknife
by: Pellegrino, Filippo
Published: (2020)
by: Pellegrino, Filippo
Published: (2020)
Wild Bootstrap Inference for Non-Negative Matrix Factorization with Random Effects
by: Satoh, Kenichi
Published: (2026)
by: Satoh, Kenichi
Published: (2026)
Bootstrapped Control Limits for Score-Based Concept Drift Control Charts
by: Wu, Jiezhong, et al.
Published: (2025)
by: Wu, Jiezhong, et al.
Published: (2025)
In-silico biological discovery with large perturbation models
by: Miladinovic, Djordje, et al.
Published: (2025)
by: Miladinovic, Djordje, et al.
Published: (2025)
Dynamical causality under invisible confounders
by: Yan, Jinling, et al.
Published: (2024)
by: Yan, Jinling, et al.
Published: (2024)
Collaborative causal inference on distributed data
by: Kawamata, Yuji, et al.
Published: (2022)
by: Kawamata, Yuji, et al.
Published: (2022)
Locally minimax optimal confidence sets for the best model
by: Kim, Ilmun, et al.
Published: (2025)
by: Kim, Ilmun, et al.
Published: (2025)
Generative Regression with IQ-BART
by: O'Hagan, Sean, et al.
Published: (2025)
by: O'Hagan, Sean, et al.
Published: (2025)
Conformal novelty detection with false discovery rate control at the boundary
by: Gao, Zijun, et al.
Published: (2026)
by: Gao, Zijun, et al.
Published: (2026)
Bootstrapping the Cross-Validation Estimate
by: Cai, Bryan, et al.
Published: (2023)
by: Cai, Bryan, et al.
Published: (2023)
Targeting relative risk heterogeneity with causal forests
by: Shirvaikar, Vik, et al.
Published: (2023)
by: Shirvaikar, Vik, et al.
Published: (2023)
A flexible Bayesian g-formula for causal survival analyses with time-dependent confounding
by: Chen, Xinyuan, et al.
Published: (2024)
by: Chen, Xinyuan, et al.
Published: (2024)
Time-uniform central limit theory and asymptotic confidence sequences
by: Waudby-Smith, Ian, et al.
Published: (2021)
by: Waudby-Smith, Ian, et al.
Published: (2021)
Sparse Bayesian Multidimensional Item Response Theory
by: Li, Jiguang, et al.
Published: (2023)
by: Li, Jiguang, et al.
Published: (2023)
Empirical Likelihood with Generative AI
by: Li, Jiguang, et al.
Published: (2026)
by: Li, Jiguang, et al.
Published: (2026)
Controlling for discrete unmeasured confounding in nonlinear causal models
by: Burauel, Patrick, et al.
Published: (2024)
by: Burauel, Patrick, et al.
Published: (2024)
Deep Bayes Factors
by: Kim, Jungeum, et al.
Published: (2023)
by: Kim, Jungeum, et al.
Published: (2023)
Similar Items
-
Causal discovery on vector-valued variables and consistency-guided aggregation
by: Ninad, Urmi, et al.
Published: (2025) -
Using Time Structure to Estimate Causal Effects
by: Hochsprung, Tom, et al.
Published: (2025) -
Separation-based distance measures for causal graphs
by: Wahl, Jonas, et al.
Published: (2024) -
Assumption violations in causal discovery and the robustness of score matching
by: Montagna, Francesco, et al.
Published: (2023) -
Causal Inference on Process Graphs, Part I: The Structural Equation Process Representation
by: Reiter, Nicolas-Domenic, et al.
Published: (2023)