Dynamical causality under invisible confounders
Fuente:
arXiv
Saved in:
| Main Authors: | Yan, Jinling, Zhang, Shao-Wu, Zhang, Chihao, Huang, Weitian, Shi, Jifan, Chen, Luonan |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Deciphering interventional dynamical causality from non-intervention complex systems
by: Shi, Jifan, et al.
Published: (2024)
by: Shi, Jifan, et al.
Published: (2024)
Parameter identification in linear non-Gaussian causal models under general confounding
by: Tramontano, Daniele, et al.
Published: (2024)
by: Tramontano, Daniele, et al.
Published: (2024)
Controlling for discrete unmeasured confounding in nonlinear causal models
by: Burauel, Patrick, et al.
Published: (2024)
by: Burauel, Patrick, et al.
Published: (2024)
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)
Valid causal inference with unobserved confounding in high-dimensional settings
by: Moosavi, Niloofar, et al.
Published: (2024)
by: Moosavi, Niloofar, et al.
Published: (2024)
Transfer Faster, Price Smarter: Minimax Dynamic Pricing under Cross-Market Preference Shift
by: Zhang, Yi, et al.
Published: (2025)
by: Zhang, Yi, et al.
Published: (2025)
Simultaneous inference for generalized linear models with unmeasured confounders
by: Du, Jin-Hong, et al.
Published: (2023)
by: Du, Jin-Hong, et al.
Published: (2023)
De-confounding Representation Learning for Counterfactual Inference on Continuous Treatment via Generative Adversarial Network
by: Zhao, Yonghe, et al.
Published: (2023)
by: Zhao, Yonghe, et al.
Published: (2023)
Nonparametric efficient inference for network quantile causal effects under partial interference
by: Cheng, Chao, et al.
Published: (2026)
by: Cheng, Chao, et al.
Published: (2026)
Doubly Robust Conditional Independence Testing with Generative Neural Networks
by: Zhang, Yi, et al.
Published: (2024)
by: Zhang, Yi, et al.
Published: (2024)
Transfer Learning for Contextual Joint Assortment-Pricing under Cross-Market Heterogeneity
by: Chen, Elynn, et al.
Published: (2026)
by: Chen, Elynn, et al.
Published: (2026)
Collaborative causal inference on distributed data
by: Kawamata, Yuji, et al.
Published: (2022)
by: Kawamata, Yuji, et al.
Published: (2022)
Causal Discovery from Heteroscedastic Stochastic Dynamical Systems under Imperfect Physical Models
by: Chen, Jianhong, et al.
Published: (2026)
by: Chen, Jianhong, et al.
Published: (2026)
Learning covariate importance for matching in policy-relevant observational research
by: Zhang, Hongzhe, et al.
Published: (2024)
by: Zhang, Hongzhe, et al.
Published: (2024)
Counterfactual identifiability beyond global monotonicity: non-monotone triangular structural causal models
by: Tan, Pengcheng, et al.
Published: (2026)
by: Tan, Pengcheng, et al.
Published: (2026)
A causal viewpoint on prediction model performance under changes in case-mix: discrimination and calibration respond differently for prognosis and diagnosis predictions
by: van Amsterdam, Wouter A. C.
Published: (2024)
by: van Amsterdam, Wouter A. C.
Published: (2024)
Separation-based distance measures for causal graphs
by: Wahl, Jonas, et al.
Published: (2024)
by: Wahl, Jonas, et al.
Published: (2024)
Targeting relative risk heterogeneity with causal forests
by: Shirvaikar, Vik, et al.
Published: (2023)
by: Shirvaikar, Vik, et al.
Published: (2023)
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)
A causal inference framework for spatial confounding
by: Gilbert, Brian, et al.
Published: (2021)
by: Gilbert, Brian, et al.
Published: (2021)
High-dimensional multiple imputation (HDMI) for partially observed confounders including natural language processing-derived auxiliary covariates
by: Weberpals, Janick, et al.
Published: (2024)
by: Weberpals, Janick, et al.
Published: (2024)
C-XGBoost: A tree boosting model for causal effect estimation
by: Kiriakidou, Niki, et al.
Published: (2024)
by: Kiriakidou, Niki, 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)
Bootstrap aggregation and confidence measures to improve time series causal discovery
by: Debeire, Kevin, et al.
Published: (2023)
by: Debeire, Kevin, 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)
Identification and multiply robust estimation in causal mediation analysis across principal strata
by: Cheng, Chao, et al.
Published: (2023)
by: Cheng, Chao, et al.
Published: (2023)
$\texttt{causalAssembly}$: Generating Realistic Production Data for Benchmarking Causal Discovery
by: Göbler, Konstantin, et al.
Published: (2023)
by: Göbler, Konstantin, et al.
Published: (2023)
Generalized coarsened confounding for causal effects: a large-sample framework
by: Ghosh, Debashis, et al.
Published: (2025)
by: Ghosh, Debashis, et al.
Published: (2025)
Learning linear acyclic causal model including Gaussian noise using ancestral relationships
by: Cai, Ming, et al.
Published: (2024)
by: Cai, Ming, et al.
Published: (2024)
Combining T-learning and DR-learning: a framework for oracle-efficient estimation of causal contrasts
by: van der Laan, Lars, et al.
Published: (2024)
by: van der Laan, Lars, et al.
Published: (2024)
Causal Representation Learning with Optimal Compression under Complex Treatments
by: Liang, Wanting, et al.
Published: (2026)
by: Liang, Wanting, et al.
Published: (2026)
Bayesian implementation of Targeted Maximum Likelihood Estimation for uncertainty quantification in causal effect estimation
by: Nannapaneni, Saideep, et al.
Published: (2025)
by: Nannapaneni, Saideep, et al.
Published: (2025)
Evaluating and Learning Optimal Dynamic Treatment Regimes under Truncation by Death
by: Park, Sihyung, et al.
Published: (2025)
by: Park, Sihyung, et al.
Published: (2025)
A confounding bridge approach for double negative control inference on causal effects
by: Miao, Wang, et al.
Published: (2018)
by: Miao, Wang, et al.
Published: (2018)
DoWhy-GCM: An extension of DoWhy for causal inference in graphical causal models
by: Blöbaum, Patrick, et al.
Published: (2022)
by: Blöbaum, Patrick, et al.
Published: (2022)
Estimation of heterogeneous principal effects under principal ignorability
by: Zhang, Rui, et al.
Published: (2026)
by: Zhang, Rui, et al.
Published: (2026)
Higher-order accurate two-sample network inference and network hashing
by: Shao, Meijia, et al.
Published: (2022)
by: Shao, Meijia, et al.
Published: (2022)
Formalising causal inference as prediction on a target population
by: Höltgen, Benedikt, et al.
Published: (2024)
by: Höltgen, Benedikt, 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)
Algorithmic syntactic causal identification
by: Cakiqi, Dhurim, et al.
Published: (2024)
by: Cakiqi, Dhurim, et al.
Published: (2024)
Similar Items
-
Deciphering interventional dynamical causality from non-intervention complex systems
by: Shi, Jifan, et al.
Published: (2024) -
Parameter identification in linear non-Gaussian causal models under general confounding
by: Tramontano, Daniele, et al.
Published: (2024) -
Controlling for discrete unmeasured confounding in nonlinear causal models
by: Burauel, Patrick, et al.
Published: (2024) -
A flexible Bayesian g-formula for causal survival analyses with time-dependent confounding
by: Chen, Xinyuan, et al.
Published: (2024) -
Valid causal inference with unobserved confounding in high-dimensional settings
by: Moosavi, Niloofar, et al.
Published: (2024)