Bayesian Networks for Causal Analysis in Socioecological Systems
This work addresses causal inference challenges for environmental and ecological researchers, but it is incremental as it extends existing Bayesian network methods to incorporate structural causal model concepts.
The paper tackles the problem of causal analysis in socioecological systems where interventional data are scarce, by applying counterfactual reasoning with Bayesian networks to a case study on socioeconomic factors and land-uses in southern Spain, resulting in an analysis of necessity and sufficiency relations between variables.
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.