期刊
INTERNATIONAL JOURNAL OF EPIDEMIOLOGY
卷 49, 期 6, 页码 2074-2082出版社
OXFORD UNIV PRESS
DOI: 10.1093/ije/dyaa049
关键词
Prediction; causal inference; generalized linear models; directed acyclic graphs; machine learning; artificial intelligence
资金
- Economic and Social Research Council [ES/J500215/1]
- Alan Turing Institute [EP/N510129/1]
- Commonwealth Scholarship Commission
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 generalized 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: (i) the covariates that should be considered for inclusion in (and possibly exclusion from) the model; (ii) how a suitable set of covariates to include in the model is determined; (iii) which covariates are ultimately selected and what functional form (i.e. parameterization) they take; (iv) how the model is evaluated; and (v) 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 that we hope will help ensure that both prediction and causal modelling are used appropriately and to greatest effect in health research.
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