This repository contains the models described in the paper "A causal inference model based on Random Forest to identify soil moisture-precipitation feedback". We developed a causal-inference model based on the Granger causality analysis and a nonlinear machine learning model. This model includes three steps: nonlinear anomaly decomposition, nonlinear Granger causality analysis, and evaluation of the quality of SM-P feedback, which eliminates the nonlinear response of interannual and seasonal variability, the memory effects of climatic factors and isolates the causal relationship of local SM-P feedback.
RFGranger is implemented by two programme language, MATLAB and python. Two editions use the same function and file names. MATLAB edition now has some bugs needs to improved.
If you use these models in your research, please cite:
@article{Lu Li,
author = {Lu Li, Wei Shangguan, Yongjiu Dai et al.},
title = {A causal inference model based on Random Forest to identify soil moisture-precipitation feedback},
journal = {Journal of Hydrometeorlogy},
year = {2020}
}Copyright (c) 2019, Lu Li