000			01450nam a2200169 a 4500
005			20260901030418.0
008			250101s2019 xx o 000 0 eng d
100	1		_aKellyn F. Arnold
245	1	0	_aGeneralised linear models for prognosis and intervention: Theory, practice, and implications for machine learning
264		1	_barXiv _c2019
336			_atext
338			_aonline resource
520			_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) th
506	0		_aOpen access — freely available to read.
856	4	0	_uhttps://arxiv.org/pdf/1906.01461v2 _yRead the full paper (PDF)
942			_cERES
999			_c718 _d718