01404nam a2200145 a 450000500170000000800400001710000210005724501180007826400160019633600090021233800200022152009050024150600460114685600660119220260901030418.0250101s2019    xx     o     000 0 eng d1 aKellyn F. Arnold10aGeneralised linear models for prognosis and intervention: Theory, practice, and implications for machine learning 1barXivc2019  atext  aonline resource  aPrediction 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) th0 aOpen access — freely available to read.40uhttps://arxiv.org/pdf/1906.01461v2yRead the full paper (PDF)