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.

因果推理和反事实推理是数据科学中的新兴方向，尤其适用于通常无法获取实验数据的环境和生态科学领域。本文提出了一个在社会生态系统领域内边界不可识别查询的新技术，并发现传统统计分析方法无法揭示变量之间的关系本质，其中反事实推理变得非常有价值。