@inproceedings{sharma2022causal,
author = {Sharma, Somya and Sharma, Swati and Neal, Andy and Malvar, Sara and Rodrigues, Eduardo and Crawford, John and Kiciman, Emre and Chandra, Ranveer},
title = {Causal Modeling of Soil Processes for Improved Generalization},
booktitle = {NeurIPS 2022 Workshop Tackling Climate Change with Machine Learning},
year = {2022},
month = {November},
abstract = {Measuring and monitoring soil organic carbon is critical for agricultural productivity and for addressing critical environmental problems. Soil organic carbon not only enriches nutrition in soil, but also has a gamut of co-benefits such as improving water storage and limiting physical erosion. Despite a litany of work in soil organic carbon estimation, current approaches do not generalize well across soil conditions and management practices. We empirically show that explicit modeling of cause-and-effect relationships among the soil processes improves the out-of-distribution generalizability of prediction models. We provide a comparative analysis of soil organic carbon estimation models where the skeleton is estimated using causal discovery methods. Our framework provide an average improvement of 81% in test mean squared error and 52% in test mean absolute error.},
url = {http://approjects.co.za/?big=en-us/research/publication/causal-modeling-of-soil-processes-for-improved-generalization/},
}