Causal Inference Based on the Analysis of Events of Relations

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.

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