Causal and counterfactual reasoning are emerging directions in data science that allow us to reason about hypothetical scenarios. This is particularly useful in fields like environmental and ecological sciences, where interventional data are usually not available. Structural causal models are probabilistic models for causal analysis that simplify this kind of reasoning due to their graphical representation. They can be regarded as extensions of the so-called Bayesian networks, a well known modeling tool commonly used in environmental and ecological problems. The main contribution of this paper is to analyze the relations of necessity and sufficiency between the variables of a socioecological system using counterfactual reasoning with Bayesian networks. In particular, we consider a case study involving socioeconomic factors and land-uses in southern Spain. In addition, this paper aims to be a coherent overview of the fundamental concepts for applying counterfactual reasoning, so that environmental researchers with a background in Bayesian networks can easily take advantage of the structural causal model formalism.

翻译：因果与反事实推理是数据科学中的新兴方向，使我们能够对假设情景进行推演。这在环境与生态科学等领域尤为有用，因为这类领域通常缺乏干预性数据。结构因果模型作为因果分析的概率模型，因其图形化表征方式简化了此类推理过程。该模型可视为贝叶斯网络的扩展形式——贝叶斯网络是环境与生态问题研究中广泛使用的经典建模工具。本文的核心贡献在于：通过贝叶斯网络的反事实推理，系统分析了社会生态系统中各变量间的必要性与充分性关系。研究特别选取西班牙南部地区社会经济因素与土地利用的案例进行实证分析。此外，本文致力于系统阐述反事实推理的基础概念框架，使具备贝叶斯网络背景的环境研究者能够顺利掌握结构因果模型的形式化方法。