01450nam a2200169 a 4500 20260901030418.0 250101s2019 xx o 000 0 eng d Kellyn F. Arnold Generalised linear models for prognosis and intervention: Theory, practice, and implications for machine learning arXiv 2019 text online resource 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 Open access — freely available to read. https://arxiv.org/pdf/1906.01461v2 Read the full paper (PDF) ERES 718 718 0 0 0 3 GEN MAIN MAIN SCICOMP 2026-09-01 0 YGE000979 2026-09-01 03:04:18 https://arxiv.org/pdf/1906.01461v2 2026-09-01 PAPER