Causal Discovery from Poisson Branching Structural Causal Model Using High-Order Cumulant with Path Analysis

arXiv:2403.16523v1 Announce Type: cross
Abstract: Count data naturally arise in many fields, such as finance, neuroscience, and epidemiology, and discovering causal structure among count data is a crucial task in various scientific and industrial scenarios. One of the most common characteristics of count data is the inherent branching structure described by a binomial thinning operator and an independent Poisson distribution that captures both branching and noise. For instance, in a population count scenario, mortality and immigration contribute to the count, …

abstract analysis arxiv causal count cs.ai cs.lg data discovery epidemiology fields finance industrial neuroscience path scientific stat.ml type