Bayesian Networks for Causal Analysis in Socioecological Systems

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 probab...

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Bibliographic Details
Published in:arXiv.org 2024-12
Main Authors: Cabañas, Rafael, Maldonado, Ana D, Morales, María, Aguilera, Pedro A, Salmerón, Antonio
Format: Article
Language:English
Subjects:
Causality
Data science
Ecological effects
Queries
Reasoning
Statistical analysis
Online Access:Get full text
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Summary: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.
ISSN:2331-8422