# Approximate Conditional Independence Test using Residuals

### Shinsuke Uda

#### 2020

#### Abstract

Conditional mutual information is a useful measure for detecting the association between variables that are also affected by other variables. Though permutation tests are used to check whether the conditional mutual information is zero to indicate mutual independence, permutations are difficult to perform because the other variables in a dataset may be associated with the variables in question. This problem is particularly acute when working with datasets of small sample size. This study aims to propose a computational method for approximating conditional mutual information based on the distribution of residuals in regression models. The proposed method can implement the permutation tests for statistical significance by translating the problem of measuring conditional independence into the problem of estimating simple independence. Additionally, a reliability of p-value in permutation test is defined to omit unreliably detected associations. We tested our proposed methodâ€™s performance in inferring the network structure of an artificial gene network against comparable methods submitted to the Dream4 challenge.

Download#### Paper Citation

#### in Harvard Style

Uda S. (2020). **Approximate Conditional Independence Test using Residuals**. In *Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,* ISBN 978-989-758-395-7, pages 297-304. DOI: 10.5220/0008866102970304

#### in Bibtex Style

@conference{icaart20,

author={Shinsuke Uda},

title={Approximate Conditional Independence Test using Residuals},

booktitle={Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},

year={2020},

pages={297-304},

publisher={SciTePress},

organization={INSTICC},

doi={10.5220/0008866102970304},

isbn={978-989-758-395-7},

}

#### in EndNote Style

TY - CONF

JO - Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,

TI - Approximate Conditional Independence Test using Residuals

SN - 978-989-758-395-7

AU - Uda S.

PY - 2020

SP - 297

EP - 304

DO - 10.5220/0008866102970304