A goal of many research programmes in biology is to extract meaningful insights from large, complex datasets. Researchers in ecology, evolution and behavior (EEB) often grapple with long-term, observational datasets from which they construct models to test causal hypotheses about biological processes. Similarly, epidemiologists analyse large, complex observational datasets to understand the distribution and determinants of human health. A key difference in the analytical workflows for these two distinct areas of biology is the delineation of data analysis tasks and explicit use of causal directed acyclic graphs (DAGs), widely adopted by epidemiologists. Here, we review the most recent causal inference literature and describe an analytical workflow that has direct applications for EEB. We start this commentary by defining four distinct analytical tasks (description, prediction, association, causal inference). The remainder of the text is dedicated to causal inference, specifically focusing on the use of DAGs to inform the modelling strategy. Given the increasing interest in causal inference and misperceptions regarding this task, we seek to facilitate an exchange of ideas between disciplinary silos and provide an analytical framework that is particularly relevant for making causal inference from observational data.

中文翻译：

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生物学家的模型选择和因果推理指南


许多生物学研究项目的目标是从大型、复杂的数据集中提取有意义的见解。生态学、进化和行为 (EEB) 领域的研究人员经常处理长期观察数据集，并从中构建模型来测试有关生物过程的因果假设。同样，流行病学家分析大型、复杂的观测数据集，以了解人类健康的分布和决定因素。这两个不同的生物学领域的分析工作流程的一个关键区别是数据分析任务的描述和流行病学家广泛采用的因果有向无环图（DAG）的明确使用。在这里，我们回顾了最新的因果推理文献，并描述了可直接应用 EEB 的分析工作流程。我们通过定义四个不同的分析任务（描述、预测、关联、因果推理）来开始这篇评论。本文的其余部分专门讨论因果推理，特别关注使用 DAG 来告知建模策略。鉴于人们对因果推断的兴趣日益浓厚，以及对这项任务的误解，我们寻求促进学科孤岛之间的思想交流，并提供一个与根据观察数据进行因果推断特别相关的分析框架。