Bayesian networks for causal analysis in socioecological systems

Abstract

Analyzing the influence of socioeconomy on land use is an important task, as socioeconomic factors can drive changes in land use that may ultimately affect human well-being. Recognizing the key factors that induce these changes may help policymakers design more effective strategies for addressing socioeconomic alterations on land-use planning, anticipate potential challenges, and mitigate negative impacts on both the environment and society. While probabilistic graphical models have been employed for this purpose in the past, this paper proposes the application of counterfactual reasoning to enhance the analysis by quantifying the degrees of necessity and sufficiency of various socioeconomic factors influencing land uses and population growth. Specifically, we present a case study using non-experimental data from southern Spain. For this, we propose the use of structural causal models, which are kind 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. This proposed approach is particularly effective for the identification of social and ecological variables that can be used in environmental monitoring and planning, offering key advantages including enhanced interpretability, and ease of adoption by environmental researchers. Our study reveals that immigration is both necessary and sufficient for population growth. In addition, built-up areas and herbaceous crops are favored by non-mountainous terrain and by high population density, whereas natural areas and mixed crops are supported by mountainous terrain and by low population density.