Causal and counterfactual reasoning are emerging directions in data science that allow us to reason about hypothetical scenarios. This is particularly useful in domains where experimental data are usually not available. In the context of environmental and ecological sciences, causality enables us, for example, to predict how an ecosystem would respond to hypothetical interventions. A structural causal model is a class of probabilistic graphical models for causality, which, due to its intuitive nature, can be easily understood by experts in multiple fields. However, certain queries, called unidentifiable, cannot be calculated in an exact and precise manner. This paper proposes applying a novel and recent technique for bounding unidentifiable queries within the domain of socioecological systems. Our findings indicate that traditional statistical analysis, including probabilistic graphical models, can identify the influence between variables. However, such methods do not offer insights into the nature of the relationship, specifically whether it involves necessity or sufficiency. This is where counterfactual reasoning becomes valuable.

翻译：因果与反事实推理是数据科学中新兴的研究方向，使我们能够对假设场景进行推理。这在实验数据通常难以获取的领域尤为有用。在环境与生态科学背景下，因果性使我们能够预测生态系统对假设干预措施的反应。结构因果模型是一类用于因果推断的概率图模型，由于其直观特性，可被多领域专家轻松理解。然而，某些被称为不可识别查询的问题无法以精确方式计算。本文提出将一种新颖先进的技术应用于社会生态系统领域，用于界定不可识别查询的范围。我们的研究结果表明，包括概率图模型在内的传统统计分析能够识别变量之间的影响关系，但此类方法无法揭示关系的本质属性——即其涉及必然性还是充分性。这正是反证推理彰显价值的领域。