Byrnes Jarrett E K, Dee Laura E
Department of Biology, University of Massachusetts Boston, Boston, Massachusetts, USA.
Department of Ecology and Evolutionary Biology, University of Colorado Boulder, Boulder, Colorado, USA.
Ecol Lett. 2025 Jan;28(1):e70023. doi: 10.1111/ele.70023.
Experiments have long been the gold standard for causal inference in Ecology. As Ecology tackles progressively larger problems, however, we are moving beyond the scales at which randomised controlled experiments are feasible. To answer causal questions at scale, we need to also use observational data -something Ecologists tend to view with great scepticism. The major challenge using observational data for causal inference is confounding variables: variables affecting both a causal variable and response of interest. Unmeasured confounders-known or unknown-lead to statistical bias, creating spurious correlations and masking true causal relationships. To combat this omitted variable bias, other disciplines have developed rigorous approaches for causal inference from observational data that flexibly control for broad suites of confounding variables. We show how ecologists can harness some of these methods-causal diagrams to identify confounders coupled with nested sampling and statistical designs-to reduce risks of omitted variable bias. Using an example of estimating warming effects on snails, we show how current methods in Ecology (e.g., mixed models) produce incorrect inferences due to omitted variable bias and how alternative methods can eliminate it, improving causal inferences with weaker assumptions. Our goal is to expand tools for causal inference using observational and imperfect experimental data in Ecology.
长期以来，实验一直是生态学中因果推断的金标准。然而，随着生态学处理的问题越来越大，我们正在超越随机对照实验可行的尺度。为了在大尺度上回答因果问题，我们还需要使用观测数据——而生态学家往往对此持高度怀疑态度。使用观测数据进行因果推断的主要挑战在于混杂变量：即那些同时影响因果变量和感兴趣的响应变量的变量。未测量的混杂因素，无论已知还是未知，都会导致统计偏差，产生虚假相关性并掩盖真实的因果关系。为了应对这种遗漏变量偏差，其他学科已经开发出了从观测数据进行因果推断的严格方法，这些方法可以灵活地控制一系列广泛的混杂变量。我们展示了生态学家如何利用其中一些方法——因果图来识别混杂因素，并结合嵌套抽样和统计设计——以降低遗漏变量偏差的风险。通过一个估计变暖对蜗牛影响的例子，我们展示了生态学中的当前方法（例如混合模型）如何由于遗漏变量偏差而产生错误的推断，以及替代方法如何消除这种偏差，在更弱的假设下改进因果推断。我们的目标是扩展在生态学中使用观测数据和不完美实验数据进行因果推断的工具。
