Predictive models aren't for causal inference,Ecology Letters

当前位置： X-MOL 学术 › Ecol. Lett. › 论文详情

Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)

Predictive models aren't for causal inference
Ecology Letters ( IF 7.6 ) Pub Date : 2022-06-07 , DOI: 10.1111/ele.14033
Suchinta Arif ₁ , M Aaron MacNeil ₁

Affiliation

Ecologists often rely on observational data to understand causal relationships. Although observational causal inference methodologies exist, predictive techniques such as model selection based on information criterion (e.g. AIC) remains a common approach used to understand ecological relationships. However, predictive approaches are not appropriate for drawing causal conclusions. Here, we highlight the distinction between predictive and causal inference and show how predictive techniques can lead to biased causal estimates. Instead, we encourage ecologists to valid causal inference methods such as the backdoor criterion, a graphical rule that can be used to determine causal relationships across observational studies.

中文翻译：

预测模型不适用于因果推理

生态学家经常依靠观察数据来理解因果关系。尽管存在观察性因果推理方法，但基于信息标准（例如 AIC）的模型选择等预测技术仍然是用于理解生态关系的常用方法。然而，预测方法不适用于得出因果结论。在这里，我们强调预测和因果推理之间的区别，并展示预测技术如何导致有偏见的因果估计。相反，我们鼓励生态学家使用有效的因果推理方法，例如后门标准，这是一种图形规则，可用于确定观察性研究中的因果关系。

更新日期：2022-06-07

点击分享查看原文

点击收藏

阅读更多本刊最新论文本刊介绍/投稿指南11