预后和干预的广义线性模型：理论、实践和对机器学习的影响
Generalised linear models for prognosis and intervention: Theory, practice, and implications for machine learning
摘要：
预测和因果解释是数据分析的两个基本任务。在健康应用中，这种差异可以从预后（预测）和预防/治疗（因果解释）之间的差异来理解。然而，这两个概念在实践中往往被混为一谈。我们使用广义线性模型（GLM）的框架来说明，预测和因果查询需要不同的过程，其应用程序和随后的解释结果。特别是，我们确定了用于预测的GLM与用于因果推断的GLM不同的五种主要方式：（1）应考虑纳入的协变量（以及可能从模型中排除）;（2）如何确定模型中包含的一组合适的协变量;（3）最终选择哪些协变量，以及什么函数形式（即参数化）;（4）如何评估模型;（5）如何解释模型。我们概述了未能承认和尊重这些差异的一些潜在后果，并考虑了对机器学习（ML）方法的影响。最后，我们提出了三项建议，我们希望这将有助于确保预测和因果模型在健康研究中得到适当的使用，并产生最大的效果。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
