Conceptos Básicos
Identifying causal structure in count data using Poisson Branching Structural Causal Model.
Resumen
The content discusses the challenges of identifying causal structures in count data, focusing on the Poisson Branching Structural Causal Model (PB-SCM). It introduces a method using high-order cumulants and path analysis to determine causal order. Theoretical results establish identifiability conditions and propose an algorithm for learning causal structure under PB-SCM.
Abstract:
- Count data arises in various fields.
- Identifying causal structure among count data is crucial.
- Challenges due to non-identifiability issue discussed.
Introduction:
- Causal discovery from observational count data is essential.
- Various methods exist but may not capture branching structures.
Data Extraction:
- "Count data naturally arise in many fields, such as finance, neuroscience, and epidemiology."
- "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."
Quotations:
- "In online services, for instance, the reason for the number of product purchases is of particular interest."
Further Questions:
How can the proposed method be applied to real-world datasets?
What are the limitations of using high-order cumulants for causal structure identification?
How does identifying causal order impact decision-making processes beyond research?
Estadísticas
"Count data naturally arise in many fields, such as finance, neuroscience, and epidemiology."
"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."
Citas
"In online services, for instance, the reason for the number of product purchases is of particular interest."