Review of counterfactual land change modeling for causal inference in land system science
Nicholas R. Magliocca, Pratik Dhungana, Carter D. Sink
Abstract
Land change models are important tools for land systems analysis, but their potential for causal inference using a counterfactual approach remains underdeveloped. This paper reviews the state of counterfactual land change modeling with the intent of causal inference. All of the reviewed studies promoted the value of creating ‘counterfactual worlds’ via simulation modeling to untangle complex causation in land systems in order to assess the effects of specific interventions and/or historical events. Several models used counterfactual analysis to challenge prevailing assumptions motivating past policy interventions, while others isolated the spatial heterogeneity of policy effects. The review also highlights methodological limitations and proposes best practices specific to counterfactual land change modeling for causal inference, such as ensemble approaches and multiple calibration-validation iterations. Counterfactual modeling is still underdeveloped in land system science, but it holds promise for advancing causal inference for some of the most challenging to study land change phenomenon.