Cover Image

Interpretable Water Level Forecaster with Spatiotemporal Causal Attention Mechanisms

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Hong, Sungchul, Choi, Yunjin, Jeon, Jong-June
Format:	Preprint
Published:	2023
Subjects:	Machine Learning Methodology
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Uniform Pessimistic Risk and its Optimal Portfolio
by: Hong, Sungchul, et al.
Published: (2023)

Improving SMOTE via Fusing Conditional VAE for Data-adaptive Noise Filtering
by: Hong, Sungchul, et al.
Published: (2024)

Deep Generative Spatiotemporal Engression for Probabilistic Forecasting of Epidemics
by: Pathak, Rajdeep, et al.
Published: (2026)

CausalFormer: An Interpretable Transformer for Temporal Causal Discovery
by: Kong, Lingbai, et al.
Published: (2024)

Lost in Aggregation: The Causal Interpretation of the IV Estimand
by: Tsao, Danielle, et al.
Published: (2026)

GST-UNet: A Neural Framework for Spatiotemporal Causal Inference with Time-Varying Confounding
by: Oprescu, Miruna, et al.
Published: (2025)

Linking Model Intervention to Causal Interpretation in Model Explanation
by: Cheng, Debo, et al.
Published: (2024)

Causal Interventional Prediction System for Robust and Explainable Effect Forecasting
by: Chu, Zhixuan, et al.
Published: (2024)

Hierarchical Bias-Driven Stratification for Interpretable Causal Effect Estimation
by: Ter-Minassian, Lucile, et al.
Published: (2024)

Dagma-DCE: Interpretable, Non-Parametric Differentiable Causal Discovery
by: Waxman, Daniel, et al.
Published: (2024)

Combining SHAP and Causal Analysis for Interpretable Fault Detection in Industrial Processes
by: Santos, Pedro Cortes dos, et al.
Published: (2025)

Causal Rule Ensemble: Interpretable Discovery and Inference of Heterogeneous Treatment Effects
by: Bargagli-Stoffi, Falco J., et al.
Published: (2020)

Penalized Empirical Likelihood for Doubly Robust Causal Inference under Contamination in High Dimensions
by: Lee, Byeonghee, et al.
Published: (2025)

Towards Accurate and Interpretable Time-series Forecasting: A Polynomial Learning Approach
by: Liu, Bo, et al.
Published: (2026)

A New Causal Rule Learning Approach to Interpretable Estimation of Heterogeneous Treatment Effect
by: Wu, Ying, et al.
Published: (2023)

Modeling Causal Mechanisms with Diffusion Models for Interventional and Counterfactual Queries
by: Chao, Patrick, et al.
Published: (2023)

Stable Differentiable Causal Discovery
by: Nazaret, Achille, et al.
Published: (2023)

Towards Causal Foundation Model: on Duality between Causal Inference and Attention
by: Zhang, Jiaqi, et al.
Published: (2023)

A VAE-based Framework for Learning Multi-Level Neural Granger-Causal Connectivity
by: Lin, Jiahe, et al.
Published: (2024)

Calibrated Multi-Level Quantile Forecasting
by: Ding, Tiffany, et al.
Published: (2025)

Interpretable Causal Inference for Analyzing Wearable, Sensor, and Distributional Data
by: Katta, Srikar, et al.
Published: (2023)

Spatiotemporal Besov Priors for Bayesian Inverse Problems
by: Lan, Shiwei, et al.
Published: (2023)

Dynamic Bayesian Learning for Spatiotemporal Mechanistic Models
by: Banerjee, Sudipto, et al.
Published: (2022)

Attention for Causal Relationship Discovery from Biological Neural Dynamics
by: Lu, Ziyu, et al.
Published: (2023)

CafeMed: Causal Attention Fusion Enhanced Medication Recommendation
by: Ren, Kelin, et al.
Published: (2025)

Causal-learn: Causal Discovery in Python
by: Zheng, Yujia, et al.
Published: (2023)

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

Local Causal Discovery for Estimating Causal Effects
by: Gupta, Shantanu, et al.
Published: (2023)

Robust Spatiotemporally Contiguous Anomaly Detection Using Tensor Decomposition
by: Mondal, Rachita, et al.
Published: (2025)

Beyond Coefficients: Forecast-Necessity Testing for Interpretable Causal Discovery in Nonlinear Time-Series Models
by: Kuskova, Valentina, et al.
Published: (2026)

Causal Additive Models with Unobserved Causal Paths and Backdoor Paths
by: Pham, Thong, et al.
Published: (2025)

From Correlation to Causation: Understanding Climate Change through Causal Analysis and LLM Interpretations
by: Shan, Shan
Published: (2024)

The Amenability Framework: Rethinking Causal Ordering Without Estimating Causal Effects
by: Fernández-Loría, Carlos, et al.
Published: (2025)

Causal Discovery of Linear Non-Gaussian Causal Models with Unobserved Confounding
by: Schkoda, Daniela, et al.
Published: (2024)

Mechanisms for Data Sharing in Collaborative Causal Inference (Extended Version)
by: Filter, Björn, et al.
Published: (2024)

Topological Causal Effects
by: Kim, Kwangho, et al.
Published: (2026)

Hierarchical Causal Models
by: Weinstein, Eli N., et al.
Published: (2024)

Causal Flow-based Variational Auto-Encoder for Disentangled Causal Representation Learning
by: Fan, Di, et al.
Published: (2023)

The Landscape of Causal Discovery Data: Grounding Causal Discovery in Real-World Applications
by: Brouillard, Philippe, et al.
Published: (2024)

Causal Graph Aided Causal Discovery in an Observational Aneurysmal Subarachnoid Hemorrhage Study
by: Berzuini, Carlo, et al.
Published: (2024)