Siegel Katherine, Dee Laura E
Cooperative Institute for Research in Environmental Sciences, University of Colorado-Boulder, Boulder, Colorado, USA.
Department of Geography, University of Colorado-Boulder, Boulder, Colorado, USA.
Ecol Lett. 2025 Jan;28(1):e70053. doi: 10.1111/ele.70053.
Ecology often seeks to answer causal questions, and while ecologists have a rich history of experimental approaches, novel observational data streams and the need to apply insights across naturally occurring conditions pose opportunities and challenges. Other fields have developed causal inference approaches that can enhance and expand our ability to answer ecological causal questions using observational or experimental data. However, the lack of comprehensive resources applying causal inference to ecological settings and jargon from multiple disciplines creates barriers. We introduce approaches for causal inference, discussing the main frameworks for counterfactual causal inference, how causal inference differs from other research aims and key challenges; the application of causal inference in experimental and quasi-experimental study designs; appropriate interpretation of the results of causal inference approaches given their assumptions and biases; foundational papers; and the data requirements and trade-offs between internal and external validity posed by different designs. We highlight that these designs generally prioritise internal validity over generalisability. Finally, we identify opportunities and considerations for ecologists to further integrate causal inference with synthesis science and meta-analysis and expand the spatiotemporal scales at which causal inference is possible. We advocate for ecology as a field to collectively define best practices for causal inference.
生态学常常试图回答因果问题，虽然生态学家有着丰富的实验方法历史，但新的观测数据流以及在自然发生的条件下应用见解的需求带来了机遇和挑战。其他领域已经开发出因果推断方法，这些方法可以增强和扩展我们利用观测或实验数据回答生态因果问题的能力。然而，缺乏将因果推断应用于生态环境的综合资源以及来自多个学科的术语造成了障碍。我们介绍因果推断的方法，讨论反事实因果推断的主要框架、因果推断与其他研究目标的不同之处以及关键挑战；因果推断在实验性和准实验性研究设计中的应用；鉴于因果推断方法的假设和偏差，对其结果进行恰当解释；基础论文；以及不同设计所带来的数据要求以及内部效度和外部效度之间的权衡。我们强调，这些设计通常优先考虑内部效度而非可推广性。最后，我们确定了生态学家进一步将因果推断与综合科学和元分析相结合并扩大因果推断可能的时空尺度的机遇和考量因素。我们倡导生态学领域共同定义因果推断的最佳实践。
