Franks Daniel W, Ruxton Graeme D, Sherratt Tom
Department of Biology, The University of York, Heslington, York, YO10 5DD, UK.
School of Biology, St Andrews University, St Andrews, KY16 9TH, UK.
Biol Rev Camb Philos Soc. 2025 May 9. doi: 10.1111/brv.70029.
Ecology has yet to embrace causal inference, yet most questions in ecology are causal. Despite the common use of terms that imply causation, such as "shapes", "drives", or "impacts", many studies shy away from directly acknowledging their causal ambitions. This avoidance not only obscures the true intent of research but also underpins a broader challenge within the field's approach to science. Ecology relies heavily on observational data, and so the necessity for robust causal inference becomes paramount. However, causal methods are also needed for non-randomised experiments. We critique the predominance in ecology of scientifically empty statistical procedures that lack scientific clarity and value. We advocate for a shift towards explicit causal inference, arguing that understanding causality is not confined to randomised controlled trials but can also be enriched through observational data when paired with rigorous causal inference methodologies. This paper elucidates the common pitfalls in ecological studies, such as throwing all variables into an analysis, use of the Akaike information criterion (AIC) for model selection, the "Table 2 fallacy" and the misuse of controls: all of which can lead to misleading scientific understanding. The good news is that causal inference is not primarily a statistical problem, but rather a scientific one that is accessible to all ecologists. We can achieve reasonable progress by continuing to use the standard statistical toolbox based around regression models, familiar to many ecologists, paired with causal diagrams. For regression, causal inference is about understanding what we should condition on (good controls) and what we should not condition on (bad controls). We provide not only a critique but a constructive guide, aiming to demystify causal inference and encourage its adoption in ecological studies using familiar approaches. By doing so, we seek to elevate the quality and impact of ecological research, moving beyond routine convenient statistical procedures and towards a more scientifically sound and insightful understanding of ecology.
生态学尚未接受因果推断，然而生态学中的大多数问题都是因果性的。尽管经常使用暗示因果关系的术语，如“塑造”“驱动”或“影响”，但许多研究都回避直接承认其因果意图。这种回避不仅掩盖了研究的真正意图，也支撑了该领域科学方法中一个更广泛的挑战。生态学严重依赖观测数据，因此稳健的因果推断的必要性变得至关重要。然而，非随机实验也需要因果方法。我们批评生态学中占主导地位的缺乏科学清晰度和价值的空洞统计程序。我们主张转向明确的因果推断，认为理解因果关系并不局限于随机对照试验，当与严格的因果推断方法相结合时，也可以通过观测数据得到丰富。本文阐明了生态研究中常见的陷阱，如将所有变量纳入分析、使用赤池信息准则（AIC）进行模型选择、“表2谬误”以及对照的滥用：所有这些都可能导致误导性的科学理解。好消息是，因果推断主要不是一个统计问题，而是一个所有生态学家都能理解的科学问题。通过继续使用许多生态学家熟悉的基于回归模型的标准统计工具箱，并结合因果图，我们可以取得合理的进展。对于回归分析，因果推断在于理解我们应该基于什么条件（好的对照）以及不应该基于什么条件（坏的对照）。我们不仅提供批评，还提供建设性的指导，旨在揭开因果推断的神秘面纱，并鼓励在生态研究中使用熟悉的方法来采用它。通过这样做，我们试图提高生态研究的质量和影响力，超越常规的便捷统计程序，朝着对生态学更科学合理且有洞察力的理解迈进。
