2010/07/14 by Kamran Karimi, Karimi, Kamran
Computer Science · #Artificial Intelligence (cs.AI) #Bayesian Modeling and Causal Inference #Constraint Satisfaction and Optimization #Data Management and Algorithms #FOS: Computer and information sciences #Machine Learning (cs.LG) #cs.AI #cs.LG
paper · pdf · doi:10.48550/arxiv.1007.2449
arxiv created 2010/07/14 · openalex publication_date 2010/07/14 · arxiv updated 2010/07/16 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Causality is a non-obvious concept that is often considered to be related to temporality. In this paper we present a number of past and present approaches to the definition of temporality and causality from philosophical, physical, and computational points of view. We note that time is an important ingredient in many relationships and phenomena. The topic is then divided into the two main areas of temporal discovery, which is concerned with finding relations that are stretched over time, and causal discovery, where a claim is made as to the causal influence of certain events on others. We present a number of computational tools used for attempting to automatically discover temporal and causal relations in data.