A causal inference perspective on the analysis of compositional data | Zendy

Kellyn F Arnold | Zendy; Laurie Berrie | Zendy; P. W. G. Tennant | Zendy; Mark S. Gilthorpe | Zendy

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A causal inference perspective on the analysis of compositional data

Author(s) -

Kellyn F Arnold,

Laurie Berrie,

P. W. G. Tennant,

Mark S. Gilthorpe

Publication year - 2020

Publication title -

international journal of epidemiology

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.406

H-Index - 208

eISSN - 1464-3685

pISSN - 0300-5771

DOI - 10.1093/ije/dyaa021

Subject(s) - causal inference , compositional data , inference , interpretation (philosophy) , context (archaeology) , directed acyclic graph , population , perspective (graphical) , econometrics , computer science , mathematics , medicine , artificial intelligence , geography , machine learning , algorithm , environmental health , archaeology , programming language

Compositional data comprise the parts of some whole, for which all parts sum to that whole. They are prevalent in many epidemiological contexts. Although many of the challenges associated with analysing compositional data have been discussed previously, we do so within a formal causal framework by utilizing directed acyclic graphs (DAGs).

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