Ordinal-Poisson Causal Discovery

Shaska, Joni; Mitra, Urbashi; Wang, Yingying; Johnson, Christine K.

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https://hdl.handle.net/2142/130307 Copy

Description

Title

Ordinal-Poisson Causal Discovery

Author(s)

• Shaska, Joni
• Mitra, Urbashi
• Wang, Yingying
• Johnson, Christine K.

Issue Date

2025-09-17

Keyword(s)

• Causal inference
• Causal discovery
• Identifiability

Abstract

This paper deals with causal discovery in the setting where random variables in the causal graph can be either ordinal or Poisson. In particular, we show that it is possible to recover the causal network (modeled by a directed acyclic graph) from purely observational data. Such results are important in the field of causal discovery since, in general, it is impossible to recover causal relationships from purely observational data, and the identifiability of causal network structures relies on assumptions regarding the data-generating process. Causal discovery in the setting where all variables in the graph are ordinal and the setting where all variables are Poisson has been separately considered. However, the setting in this paper where the two are allowed to co-exist in the graph has, to the best of our knowledge, not been considered. In addition to proving the identifiability of ordinal-Poisson causal discovery, we show, through numerical simulations, that the Bayesian information criterion (BIC) tends to reliably recover the true causal graph as the number of observations increases.

Publisher

Allerton Conference on Communication, Control, and Computing

Series/Report Name or Number

2025 61st Allerton Conference on Communication, Control, and Computing Proceedings

ISSN

2836-4503

Type of Resource

Text

Genre of Resource

Conference Paper/Presentation

Language

eng

Handle URL

https://hdl.handle.net/2142/130307&&

Copyright and License Information

Copyright 2025 owned by the authors.

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