A pressing question resulting from global warming is how climate change will affect infectious diseases. Answering this question requires research into the effects of weather on the population dynamics of transmission and infection; elucidating these effects, however, has proved difficult due to the challenges of assessing causality from the predominantly observational data available in epidemiological research. Here we show how concepts from causal inference—the sub-field of statistics aiming at inferring causality from data—can guide that research. Through a series of case studies, we illustrate how such concepts can help assess study design and strategically choose a study’s location, evaluate and reduce the risk of bias, and interpret the multifaceted effects of meteorological variables on transmission. More broadly, we argue that interdisciplinary approaches based on explicit causal frameworks are crucial for reliably estimating the effect of weather and accurately predicting the consequences of climate change.

全球变暖导致的一个紧迫问题是气候变化将如何影响传染病。回答这个问题需要研究天气对传播和感染的人口动态的影响;然而，由于从流行病学研究中的主要观察数据评估因果关系的挑战，阐明这些影响已被证明是困难的。在这里，我们展示了因果推理的概念——旨在从数据中推断因果关系的统计学子领域——如何指导该研究。通过一系列案例研究，我们说明了这些概念如何帮助评估研究设计和战略性地选择研究地点、评估和降低偏倚风险，以及解释气象变量对传播的多方面影响。更广泛地说，我们认为基于明确因果框架的跨学科方法对于可靠地估计天气的影响和准确预测气候变化的后果至关重要。