Temporal Causal Discovery

Temporal causal discovery aims to identify cause-and-effect relationships within time-series data, a crucial task for understanding complex systems across various domains. Current research focuses on developing sophisticated algorithms, including transformer-based models and constraint-based approaches, to address challenges like high dimensionality, autocorrelation, and non-stationarity in the data, often incorporating interventional data or leveraging large language models for improved accuracy. These advancements are improving the ability to extract meaningful causal insights from temporal data, with significant implications for fields like healthcare, industrial operations, and finance, enabling better prediction, diagnosis, and decision-making.

Papers

October 25, 2024

Robust Time Series Causal Discovery for Agent-Based Model Validation
Gene Yu, Ce Guo, Wayne Luk
Causal Discovery Agent Based Model Causal Discovery Method Temporal Causal Discovery

June 24, 2024

CausalFormer: An Interpretable Transformer for Temporal Causal Discovery
Lingbai Kong, Wengen Li, Hanchen Yang, Yichao Zhang, Jihong Guan, Shuigeng Zhou
Causal Discovery Method Causal Transformer Interpretable Transformer Temporal Causal Discovery

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

March 17, 2023

Causal Discovery from Temporal Data: An Overview and New Perspectives
Chang Gong, Di Yao, Chuzhe Zhang, Wenbin Li, Jingping Bi
Causal Discovery New Perspective Temporal Data Causal Discovery Method Temporal Causal Discovery

March 6, 2023

eCDANs: Efficient Temporal Causal Discovery from Autocorrelated and Non-stationary Data (Student Abstract)
Muhammad Hasan Ferdous, Uzma Hasan, Md Osman Gani
Causal Relationship Conditional Independence Causal Relation Non Stationary Data Temporal Causal Discovery

February 7, 2023

CDANs: Temporal Causal Discovery from Autocorrelated and Non-Stationary Time Series Data
Muhammad Hasan Ferdous, Uzma Hasan, Md Osman Gani
Time Series Causal Discovery Causal Relationship Causal Relation Causal Discovery Method Constraint Based Causal Discovery Temporal Causal Discovery