Temporal Causal

Temporal causal analysis focuses on identifying and modeling cause-and-effect relationships that unfold over time. Current research emphasizes developing algorithms and models, including graph attention networks and those leveraging large language models, to discover these temporal causal structures from observational data, often incorporating interventional data or domain knowledge to improve accuracy and efficiency. This work is crucial for advancing fields like AI for IT operations (root cause analysis), reinforcement learning (improving agent behavior and reducing exploration), and video understanding (enhancing action detection), where understanding temporal causality is essential for improved performance and interpretability.

Papers

October 28, 2024

Active Causal Structure Learning with Latent Variables: Towards Learning to Detour in Autonomous Robots
Pablo de los Riscos, Fernando Corbacho
Latent Variable Autonomous Robot Causal Model Structural Causal Model Temporal Causal Active Causal

July 25, 2024

Harnessing Temporal Causality for Advanced Temporal Action Detection
Shuming Liu, Lin Sui, Chen-Lin Zhang, Fangzhou Mu, Chen Zhao, Bernard Ghanem
Action Recognition Temporal Action Detection Action Detection Causal Attention Temporal Causal

April 23, 2024

RealTCD: Temporal Causal Discovery from Interventional Data with Large Language Model
Peiwen Li, Xin Wang, Zeyang Zhang, Yuan Meng, Fang Shen, Yue Li, Jialong Wang, Yang Li, Wenweu Zhu
Large Language Model Causal Discovery Causal Discovery Method Interventional Data Temporal Causal Temporal Causal Discovery

October 24, 2023

Causal Representation Learning Made Identifiable by Grouping of Observational Variables
Hiroshi Morioka, Aapo Hyvärinen
Causal Discovery Causal Model Group Setting Causal Representation Learning Observational Data Latent Causal Temporal Causal

July 16, 2023

Heteroscedastic Causal Structure Learning
Bao Duong, Thin Nguyen
Causal Ordering Causal DAG Temporal Causal

June 23, 2023

Reinforcement Learning with Temporal-Logic-Based Causal Diagrams
Yash Paliwal, Rajarshi Roy, Jean-Raphaël Gaglione, Nasim Baharisangari, Daniel Neider, Xiaoming Duan, Ufuk Topcu, Zhe Xu
Reinforcement Learning Causal Graph Reinforcement Learning Algorithm Causal Knowledge Temporal Causal

May 31, 2023

Active causal structure learning with advice
Davin Choo, Themis Gouleakis, Arnab Bhattacharyya
Learning Algorithm Worst Case Causal Direction Advice Quality Temporal Causal Ancestral Graph

April 21, 2023

TC-GAT: Graph Attention Network for Temporal Causality Discovery
Xiaosong Yuan, Ke Chen, Wanli Zuo, Yijia Zhang
Graph Attention Network Causal Knowledge Event Causality Identification Causality Extraction Causal Relation Extraction Temporal Causal

March 27, 2023

Causal schema induction for knowledge discovery
Michael Regan, Jena D. Hwang, Keisuke Sakaguchi, James Pustejovsky
Knowledge Discovery Temporal Causal Schema Induction

March 24, 2023

Causality Detection for Efficient Multi-Agent Reinforcement Learning
Rafael Pina, Varuna De Silva, Corentin Artaud
Multi Agent Reinforcement Learning Causal Effect Estimation Sub Optimal Policy Causality Detection Temporal Causal Lazy Agent

March 12, 2023

DOMINO: Visual Causal Reasoning with Time-Dependent Phenomena
Jun Wang, Klaus Mueller
Causal Relation Causal Network DominO Denoise Temporal Causal Visual Linguistic Causal Generating Process

December 6, 2021

Temporal-Spatial Causal Interpretations for Vision-Based Reinforcement Learning
Wenjie Shi, Gao Huang, Shiji Song, Cheng Wu
Vision Based Reinforcement Learning Temporal Causal Temporal Spatial Causal