Learning Causes of Functional Dynamic Targets: Screening and Local Methods

• Published in: Entropy (Basel, Switzerland) Vol. 26; no. 7; p. 541
• Main Authors: Zhao, Ruiqi, Yang, Xiaoxia, He, Yangbo
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.07.2024
• Subjects: Algorithms; Analysis; direct causes; functional dynamic target; Functionals; indirect causes; local structure learning; Machine learning; Methods; Random variables; Screening; screening method; Statistical methods; Synthetic data
• ISSN: 1099-4300; 1099-4300
• DOI: 10.3390/e26070541

This paper addresses the challenge of identifying causes for functional dynamic targets, which are functions of various variables over time. We develop screening and local learning methods to learn the direct causes of the target, as well as all indirect causes up to a given distance. We first discuss the modeling of the functional dynamic target. Then, we propose a screening method to select the variables that are significantly correlated with the target. On this basis, we introduce an algorithm that combines screening and structural learning techniques to uncover the causal structure among the target and its causes. To tackle the distance effect, where long causal paths weaken correlation, we propose a local method to discover the direct causes of the target in these significant variables and further sequentially find all indirect causes up to a given distance. We show theoretically that our proposed methods can learn the causes correctly under some regular assumptions. Experiments based on synthetic data also show that the proposed methods perform well in learning the causes of the target.