Bayesian Networks for Causal Analysis in Socioecological Systems (2401.10101v2)

Abstract: 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.

Understanding Causality in Socioecological Systems

Introduction to Counterfactual Reasoning in Environmental Science

Causal and counterfactual reasoning are analytical approaches that allow scientists to understand how changes or interventions could potentially affect a system. Particularly in environmental and ecological sciences, such analysis is crucial. Causal reasoning, previously dependent on randomized experiments, can now be implemented through analytical methods that accommodate observational data. This is especially important in fields where direct experimentation is unfeasible. A notable example involves understanding how a species' population impacts ecosystem structure—an intervention that is ethically and practically impossible.

Linking Probabilistic Graphical Models with Environmental Data

The paper focuses on structural causal models (SCMs), a subset of probabilistic graphical models (PGMs), as a method for investigating relationships within socioecological systems. These models provide a platform that translates expert knowledge into intuitive diagrams. Researchers can extrapolate potential effects of hypothetical scenarios by using PGMs, which have been effectively applied in a wide range of environmental studies to control confounding factors and missing data.

Novel Approaches in Analyzing Socioecological Data

To demonstrate the application's potential in socioecological systems analysis, researchers utilized a data-set reflecting socio-economic factors and land uses across municipalities in Andalusia, Spain. They employed a novel technique—expectation-maximization for causal computation (EMCC)—which is particularly suited to handle unidentifiable queries from observational data. The method converts SCMs into actionable models by estimating probabilities for unobservable variables.

Findings and Implications for Policy and Management

The application of EMCC on the Andalusian data revealed that immigration is both a necessary and sufficient condition for population increase. Location was determined as vital for land use outcomes—mountainous areas with low population density were required for increasing natural or mixed land uses. Further, high population density in non-mountainous regions was necessary for the presence of built areas and greenhouse farming. Counterfactual reasoning provided deeper insights compared to traditional methods, uncovering relationships that statistical correlation could not.

Overall, leveraging counterfactual reasoning in socioecological system analysis opens a window to more nuanced policy making. Decision-makers can now better predict the impacts of interventions on environmental ecosystems and take pre-emptive measures. The developed method sets a solid groundwork for futuristically integrating counterfactual reasoning into environmental management and planning.