{"total":244,"rows":[{"id":"2203.16797","title":"When Physics Meets Machine Learning: A Survey of Physics-Informed Machine Learning","created":"2022-03-31","cited_by_count":65,"area":null},{"id":"2111.15155","title":"gCastle: A Python Toolbox for Causal Discovery","created":"2021-11-30","cited_by_count":61,"area":null},{"id":"2206.05871","title":"Causal Inference-Based Root Cause Analysis for Online Service Systems with Intervention Recognition","created":"2022-06-13","cited_by_count":56,"area":null},{"id":"2301.06574","title":"Causal Recurrent Variational Autoencoder for Medical Time Series Generation","created":"2023-01-16","cited_by_count":45,"area":null},{"id":"2303.10112","title":"Causal Discovery from Temporal Data: An Overview and New Perspectives","created":"2023-03-17","cited_by_count":43,"area":null},{"id":"2305.05890","title":"CUTS+: High-dimensional Causal Discovery from Irregular Time-series","created":"2023-05-10","cited_by_count":41,"area":null},{"id":"2101.09126","title":"Will Artificial Intelligence supersede Earth System and Climate Models?","created":"2021-01-22","cited_by_count":37,"area":null},{"id":"2305.13341","title":"Discovering Causal Relations and Equations from Data","created":"2023-05-21","cited_by_count":36,"area":null},{"id":"2310.01753","title":"CausalTime: Realistically Generated Time-series for Benchmarking of Causal Discovery","created":"2023-10-03","cited_by_count":36,"area":null},{"id":"2303.15027","title":"A Survey on Causal Discovery Methods for I.I.D. and Time Series Data","created":"2023-03-27","cited_by_count":35,"area":null},{"id":"2109.04286","title":"NTS-NOTEARS: Learning Nonparametric DBNs With Prior Knowledge","created":"2021-09-09","cited_by_count":33,"area":null},{"id":"2302.07458","title":"CUTS: Neural Causal Discovery from Irregular Time-Series Data","created":"2023-02-15","cited_by_count":33,"area":null},{"id":"2210.14706","title":"Rhino: Deep Causal Temporal Relationship Learning With History-dependent Noise","created":"2022-10-26","cited_by_count":30,"area":null},{"id":"2303.04038","title":"Root Cause Identification for Collective Anomalies in Time Series given an Acyclic Summary Causal Graph with Loops","created":"2023-03-07","cited_by_count":30,"area":null},{"id":"2310.13387","title":"Assumption violations in causal discovery and the robustness of score matching","created":"2023-10-20","cited_by_count":28,"area":null},{"id":"2104.08043","title":"Data Generating Process to Evaluate Causal Discovery Techniques for Time Series Data","created":"2021-04-16","cited_by_count":22,"area":null},{"id":"2105.10884","title":"THP: Topological Hawkes Processes for Learning Causal Structure on Event Sequences","created":"2021-05-23","cited_by_count":19,"area":null},{"id":"2204.01819","title":"Achieving Long-Term Fairness in Sequential Decision Making","created":"2022-04-04","cited_by_count":19,"area":null},{"id":"2402.18477","title":"Signature Kernel Conditional Independence Tests in Causal Discovery for Stochastic Processes","created":"2024-02-28","cited_by_count":18,"area":null},{"id":"2406.16708","title":"CausalFormer: An Interpretable Transformer for Temporal Causal Discovery","created":"2024-06-24","cited_by_count":17,"area":null},{"id":"2106.05194","title":"DIGRAC: Digraph Clustering Based on Flow Imbalance","created":"2021-06-09","cited_by_count":15,"area":null},{"id":"2106.12430","title":"Beyond Predictions in Neural ODEs: Identification and Interventions","created":"2021-06-23","cited_by_count":15,"area":null},{"id":"2304.04661","title":"AI for IT Operations (AIOps) on Cloud Platforms: Reviews, Opportunities and Challenges","created":"2023-04-10","cited_by_count":15,"area":null},{"id":"2210.16535","title":"Causal Discovery of Dynamic Models for Predicting Human Spatial Interactions","created":"2022-10-29","cited_by_count":14,"area":null},{"id":"2305.05986","title":"Structural Hawkes Processes for Learning Causal Structure from Discrete-Time Event Sequences","created":"2023-05-10","cited_by_count":14,"area":null},{"id":"2307.15678","title":"Case Studies of Causal Discovery from IT Monitoring Time Series","created":"2023-07-28","cited_by_count":14,"area":null},{"id":"2310.14691","title":"Identifiability of total effects from abstractions of time series causal graphs","created":"2023-10-23","cited_by_count":14,"area":null},{"id":"2311.03309","title":"Neural Structure Learning with Stochastic Differential Equations","created":"2023-11-06","cited_by_count":14,"area":null},{"id":"2306.07019","title":"Dynamic Causal Graph Convolutional Network for Traffic Prediction","created":"2023-06-12","cited_by_count":12,"area":null},{"id":"2306.07024","title":"DRCFS: Doubly Robust Causal Feature Selection","created":"2023-06-12","cited_by_count":12,"area":null},{"id":"2201.08283","title":"Lead-lag detection and network clustering for multivariate time series with an application to the US equity market","created":"2022-01-20","cited_by_count":11,"area":null},{"id":"2403.11960","title":"Causality-Aware Spatiotemporal Graph Neural Networks for Spatiotemporal Time Series Imputation","created":"2024-03-18","cited_by_count":11,"area":null},{"id":"2208.01529","title":"CIPCaD-Bench: Continuous Industrial Process datasets for benchmarking Causal Discovery methods","created":"2022-08-02","cited_by_count":10,"area":null},{"id":"2310.06312","title":"Discovering Mixtures of Structural Causal Models from Time Series Data","created":"2023-10-10","cited_by_count":10,"area":null},{"id":"2506.01361","title":"TimeGraph: Synthetic Benchmark Datasets for Robust Time-Series Causal Discovery","created":"2025-06-02","cited_by_count":10,"area":null},{"id":"2105.10381","title":"Entropy-based Discovery of Summary Causal Graphs in Time Series","created":"2021-05-21","cited_by_count":9,"area":null},{"id":"2402.04952","title":"Separation-based distance measures for causal graphs","created":"2024-02-07","cited_by_count":9,"area":null},{"id":"2405.08779","title":"Jacobian Regularizer-based Neural Granger Causality","created":"2024-05-14","cited_by_count":9,"area":null},{"id":"2110.09887","title":"Time Series Analysis via Network Science: Concepts and Algorithms","created":"2021-10-11","cited_by_count":8,"area":null},{"id":"2306.08765","title":"Causal Discovery from Time Series with Hybrids of Constraint-Based and Noise-Based Algorithms","created":"2023-06-14","cited_by_count":8,"area":null},{"id":"2306.08946","title":"Bootstrap aggregation and confidence measures to improve time series causal discovery","created":"2023-06-15","cited_by_count":8,"area":null},{"id":"2505.16620","title":"CausalDynamics: A large-scale benchmark for structural discovery of dynamical causal models","created":"2025-05-22","cited_by_count":8,"area":null},{"id":"2207.07908","title":"Multiscale Causal Structure Learning","created":"2022-07-16","cited_by_count":7,"area":null},{"id":"2302.03246","title":"CDANs: Temporal Causal Discovery from Autocorrelated and Non-Stationary Time Series Data","created":"2023-02-07","cited_by_count":7,"area":null},{"id":"2403.08414","title":"Causal Graph Neural Networks for Wildfire Danger Prediction","created":"2024-03-13","cited_by_count":7,"area":null},{"id":"2407.07291","title":"Causal Discovery in Semi-Stationary Time Series","created":"2024-07-10","cited_by_count":7,"area":null},{"id":"2501.12500","title":"Learning General Causal Structures with Hidden Dynamic Process for Climate Analysis","created":"2025-01-21","cited_by_count":7,"area":null},{"id":"2503.04852","title":"CAUSAL3D: A Comprehensive Benchmark for Causal Learning from Visual Data","created":"2025-03-06","cited_by_count":7,"area":null},{"id":"2107.01126","title":"Interactive Causal Structure Discovery in Earth System Sciences","created":"2021-07-01","cited_by_count":6,"area":null},{"id":"2302.00293","title":"A Survey of Methods, Challenges and Perspectives in Causality","created":"2023-02-01","cited_by_count":6,"area":null},{"id":"2306.11498","title":"Conditional Independence Testing with Heteroskedastic Data and Applications to Causal Discovery","created":"2023-06-20","cited_by_count":6,"area":null},{"id":"2312.02858","title":"Towards Causal Representations of Climate Model Data","created":"2023-12-05","cited_by_count":6,"area":null},{"id":"2506.03964","title":"Causality-Aware Contrastive Learning for Robust Multivariate Time-Series Anomaly Detection","created":"2025-06-04","cited_by_count":6,"area":null},{"id":"2301.13516","title":"Recurrences reveal shared causal drivers of complex time series","created":"2023-01-31","cited_by_count":5,"area":null},{"id":"2302.00064","title":"Evaluating Temporal Observation-Based Causal Discovery Techniques Applied to Road Driver Behaviour","created":"2023-01-31","cited_by_count":5,"area":null},{"id":"2307.16048","title":"Structural restrictions in local causal discovery: identifying direct causes of a target variable","created":"2023-07-29","cited_by_count":5,"area":null},{"id":"2310.11154","title":"Causal discovery using dynamically requested knowledge","created":"2023-10-17","cited_by_count":5,"area":null},{"id":"2312.07199","title":"SeasFire as a Multivariate Earth System Datacube for Wildfire Dynamics","created":"2023-12-12","cited_by_count":5,"area":null},{"id":"2403.11219","title":"Causality from Bottom to Top: A Survey","created":"2024-03-17","cited_by_count":5,"area":null},{"id":"2406.05805","title":"Towards identifiability of micro total effects in summary causal graphs with latent confounding: extension of the front-door criterion","created":"2024-06-09","cited_by_count":5,"area":null},{"id":"2410.07013","title":"Causal Representation Learning in Temporal Data via Single-Parent Decoding","created":"2024-10-09","cited_by_count":5,"area":null},{"id":"2506.09891","title":"Causal Climate Emulation with Bayesian Filtering","created":"2025-06-11","cited_by_count":5,"area":null},{"id":"2203.15470","title":"Graph similarity learning for change-point detection in dynamic networks","created":"2022-03-29","cited_by_count":4,"area":null},{"id":"2207.04055","title":"Causal Discovery using Model Invariance through Knockoff Interventions","created":"2022-07-08","cited_by_count":4,"area":null},{"id":"2209.03427","title":"Causal discovery for time series with latent confounders","created":"2022-09-07","cited_by_count":4,"area":null},{"id":"2302.01416","title":"Neural Insights for Digital Marketing Content Design","created":"2023-02-02","cited_by_count":4,"area":null},{"id":"2304.05294","title":"Selecting Robust Features for Machine Learning Applications using Multidata Causal Discovery","created":"2023-04-11","cited_by_count":4,"area":null},{"id":"2311.06012","title":"Double Machine Learning Based Structure Identification from Temporal Data","created":"2023-11-10","cited_by_count":4,"area":null},{"id":"2402.16068","title":"ROS-Causal: A ROS-based Causal Analysis Framework for Human-Robot Interaction Applications","created":"2024-02-25","cited_by_count":4,"area":null},{"id":"2406.10419","title":"Learning Flexible Time-windowed Granger Causality Integrating Heterogeneous Interventional Time Series Data","created":"2024-06-14","cited_by_count":4,"area":null},{"id":"2504.08016","title":"Emergence of psychopathological computations in large language models","created":"2025-04-10","cited_by_count":4,"area":null},{"id":"2512.02193","title":"From monoliths to modules: Decomposing transducers for efficient world modelling","created":"2025-12-01","cited_by_count":4,"area":"wm"},{"id":"2106.00842","title":"Leveraging Pre-Images to Discover Nonlinear Relationships in Multivariate Environments","created":"2021-06-01","cited_by_count":3,"area":null},{"id":"2109.10817","title":"Causal Inference in Non-linear Time-series using Deep Networks and Knockoff Counterfactuals","created":"2021-09-22","cited_by_count":3,"area":null},{"id":"2303.06423","title":"Learning interpretable causal networks from very large datasets, application to 400,000 medical records of breast cancer patients","created":"2023-03-11","cited_by_count":3,"area":null},{"id":"2303.09634","title":"Causal Temporal Graph Convolutional Neural Networks (CTGCN)","created":"2023-03-16","cited_by_count":3,"area":null},{"id":"2310.09091","title":"Insightful analysis of historical sources at scales beyond human capabilities using unsupervised Machine Learning and XAI","created":"2023-10-13","cited_by_count":3,"area":null},{"id":"2310.11059","title":"Causal Feature Selection via Transfer Entropy","created":"2023-10-17","cited_by_count":3,"area":null},{"id":"2406.04955","title":"Experimental Evaluation of ROS-Causal in Real-World Human-Robot Spatial Interaction Scenarios","created":"2024-06-07","cited_by_count":3,"area":null},{"id":"2410.02844","title":"CAnDOIT: Causal Discovery with Observational and Interventional Data from Time-Series","created":"2024-10-03","cited_by_count":3,"area":null},{"id":"2411.06990","title":"Causal-discovery-based root-cause analysis and its application in time-series prediction error diagnosis","created":"2024-11-11","cited_by_count":3,"area":null},{"id":"2501.14291","title":"Advances in Temporal Point Processes: Bayesian, Neural, and LLM Approaches","created":"2025-01-24","cited_by_count":3,"area":null},{"id":"2506.17065","title":"Flow based approach for Dynamic Temporal Causal models with non-Gaussian or Heteroscedastic Noises","created":"2025-06-20","cited_by_count":3,"area":null},{"id":"2511.17505","title":"Causal Intervention Sequence Analysis for Fault Tracking in Radio Access Networks","created":"2025-10-02","cited_by_count":3,"area":null},{"id":"2110.07785","title":"Scalable Causal Structure Learning: Scoping Review of Traditional and Deep Learning Algorithms and New Opportunities in Biomedicine","created":"2021-10-15","cited_by_count":2,"area":null},{"id":"2111.09666","title":"CCSL: A Causal Structure Learning Method from Multiple Unknown Environments","created":"2021-11-18","cited_by_count":2,"area":null},{"id":"2206.05175","title":"A Causal Research Pipeline and Tutorial for Psychologists and Social Scientists","created":"2022-06-10","cited_by_count":2,"area":null},{"id":"2209.05598","title":"Learning domain-specific causal discovery from time series","created":"2022-09-12","cited_by_count":2,"area":null},{"id":"2212.14133","title":"Investigating Sindy As a Tool For Causal Discovery In Time Series Signals","created":"2022-12-29","cited_by_count":2,"area":null},{"id":"2301.10859","title":"Salesforce CausalAI Library: A Fast and Scalable Framework for Causal Analysis of Time Series and Tabular Data","created":"2023-01-25","cited_by_count":2,"area":null},{"id":"2305.03662","title":"Causal Discovery with Stage Variables for Health Time Series","created":"2023-05-05","cited_by_count":2,"area":null},{"id":"2311.01412","title":"Causal Temporal Regime Structure Learning","created":"2023-11-02","cited_by_count":2,"area":null},{"id":"2312.14333","title":"Behaviour Modelling of Social Animals via Causal Structure Discovery and Graph Neural Networks","created":"2023-12-21","cited_by_count":2,"area":null},{"id":"2401.07231","title":"Use of Prior Knowledge to Discover Causal Additive Models with Unobserved Variables and its Application to Time Series Data","created":"2024-01-14","cited_by_count":2,"area":null},{"id":"2402.06500","title":"On the Fly Detection of Root Causes from Observed Data with Application to IT Systems","created":"2024-02-09","cited_by_count":2,"area":null},{"id":"2405.08699","title":"Weakly-supervised causal discovery based on fuzzy knowledge and complex data complementarity","created":"2024-05-14","cited_by_count":2,"area":null},{"id":"2406.03212","title":"Inferring the time-varying coupling of dynamical systems with temporal convolutional autoencoders","created":"2024-06-05","cited_by_count":2,"area":null},{"id":"2407.05869","title":"PORCA: Root Cause Analysis with Partially Observed Data","created":"2024-07-08","cited_by_count":2,"area":null},{"id":"2407.07290","title":"Causal Discovery-Driven Change Point Detection in Time Series","created":"2024-07-10","cited_by_count":2,"area":null},{"id":"2411.06391","title":"CausalStock: Deep End-to-end Causal Discovery for News-driven Stock Movement Prediction","created":"2024-11-10","cited_by_count":2,"area":null}],"next_cursor":"100"}