B0247
Title: Robustly modeling the nonlinear impact of climate change on agriculture by combining econometrics and machine learning
Authors: Benedetta Francesconi - Vrije Universiteit Amsterdam & University of Luxembourg (Netherlands) [presenting]
Ying-Jung Chen - Descartes Labs, Georgia Institute of Technology (United States)
Abstract: Climate change is expected to have a dramatic impact on agricultural production; however, due to natural complexity, the exact avenues and relative strengths by which this will happen are still unknown. The development of accurate forecasting models is thus of great importance to enable policymakers to design effective interventions. To date, most machine learning methods aimed at tackling this problem lack consideration of causal structure, thereby making them unreliable for the types of counterfactual analysis necessary when making policy decisions. Econometrics has developed robust techniques for estimating cause-effect relations in time series, specifically through the use of cointegration analysis and Granger causality. However, these methods are frequently limited in flexibility, especially in the estimation of nonlinear relationships. Integrating the non-linear function approximators with the robust causal estimation methods is proposed to ultimately develop an accurate agricultural forecasting model capable of robust counterfactual analysis. This method would be a valuable new asset for government and industrial stakeholders to understand how climate change impacts agricultural production.
