  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.
