Causal Inference Based on the Analysis of Events of Relations

Key Laboratory for NeuroInformation of Ministry of Education
School of Life Science and Technology
University of Electronic Science and Technology of China
No. 4, Section 2, North Jianshe Road, 610054, Chengdu, Sichuan, P.R.China

The main concept behind causality involves both statistical conditions and temporal relations. However, various current approaches to causal inference, focusing on probability vs. conditional probability, are based on model functions or parametric estimations. These approaches are not appropriate when addressing non-stationary variables. In this work, we propose a Causal inference approach based on the analysis of Events of Relations (CER). CER focuses on the temporal delay relation between cause and effect, and a binomial test is established to determine whether an “event of relation” with a non-zero delay is significantly different than one with zero delay. Because CER avoids parameter estimation using sampling data, the method can be applied to both stationary and non-stationary neural signals.

top of the pag


    I. Orignial Paper

  1. "Causal Inference Based on the Analysis of Events of Relations for Non-stationary Neural Signals ". Yu Yin, Dezhong Yao.

    II. Other Paper


© Key Laboratory for NeuroInformation, Ministry of Education, China

Flag Counter Flag Counter
XML 地图 | Sitemap 地图