  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) they take; (4) How
the model is evaluated; and (5) How the model is interpreted. We outline some
of the potential consequences of failing to acknowledge and respect these
differences, and additionally consider the implications for machine learning
(ML) methods. We then conclude with three recommendations which we hope will
help ensure that both prediction and causal modelling are used appropriately
and to greatest effect in health research.
