Reflection on modern methods: generalized linear models for prognosis and intervention—theory, practice and implications for machine learning

Volume: 49, Issue: 6, Pages: 2074 - 2082
Published: May 7, 2020
Abstract
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...
Paper Details
Title
Reflection on modern methods: generalized linear models for prognosis and intervention—theory, practice and implications for machine learning
Published Date
May 7, 2020
Volume
49
Issue
6
Pages
2074 - 2082
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