Generalised linear models for prognosis and intervention: Theory, practice, and implications for machine learning (Record no. 718)

000 -LEADER
fixed length control field 01450nam a2200169 a 4500
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20260901030418.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 250101s2019 xx o 000 0 eng d
100 1# - MAIN ENTRY--PERSONAL NAME
Personal name Kellyn F. Arnold
245 10 - TITLE STATEMENT
Title Generalised linear models for prognosis and intervention: Theory, practice, and implications for machine learning
264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE
Name of producer, publisher, distributor, manufacturer arXiv
Date of production, publication, distribution, manufacture, or copyright notice 2019
336 ## - CONTENT TYPE
Content type term text
338 ## - CARRIER TYPE
Carrier type term online resource
520 ## - SUMMARY, ETC.
Summary, etc. Prediction and causal explanation are fundamentally distinct tasks of data analysis. In health applications, this difference can be understood in terms of the difference between prognosis (prediction) and prevention/treatment (causal explanation). Nevertheless, these two concepts are often conflated in practice. We use the framework of generalised linear models (GLMs) to illustrate that predictive and causal queries require distinct processes for their application and subsequent interpretation of results. In particular, we identify five primary ways in which GLMs for prediction differ from GLMs for causal inference: (1) The covariates that should be considered for inclusion in (and possibly exclusion from) the model; (2) How a suitable set of covariates to include in the model is determined; (3) Which covariates are ultimately selected, and what functional form (i.e. parameterisation) th
506 0# - RESTRICTIONS ON ACCESS NOTE
Terms governing access Open access — freely available to read.
856 40 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier <a href="https://arxiv.org/pdf/1906.01461v2">https://arxiv.org/pdf/1906.01461v2</a>
Link text Read the full paper (PDF)
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      Available online General Yegates University Library Yegates University Library Science and Computing 09/01/2026   YGE000979 09/01/2026 https://arxiv.org/pdf/1906.01461v2 09/01/2026 Research paper — read online