Knowledge Guided Representation Learning and Causal Structure Learning
  in Soil Science
Somya Sharma Swati Sharma Licheng Liu Rishabh Tushir Andy Neal Robert Ness John Crawford Emre Kiciman Ranveer Chandra
An improved understanding of soil can enable more sustainable land-use
practices. Nevertheless, soil is called a complex, living medium due to the
complex interaction of different soil processes that limit our understanding of
soil. Process-based models and analyzing observed data provide two avenues for
improving our understanding of soil processes. Collecting observed data is
cost-prohibitive but reflects real-world behavior, while process-based models
can be used to generate ample synthetic data which may not be representative of
reality. We propose a framework, knowledge-guided representation learning, and
causal structure learning (KGRCL), to accelerate scientific discoveries in soil
science. The framework improves representation learning for simulated soil
processes via conditional distribution matching with observed soil processes.
Simultaneously, the framework leverages both observed and simulated data to
learn a causal structure among the soil processes. The learned causal graph is
more representative of ground truth than other graphs generated from other
causal discovery methods. Furthermore, the learned causal graph is leveraged in
a supervised learning setup to predict the impact of fertilizer use and
changing weather on soil carbon. We present the results in five different
locations to show the improvement in the prediction performance in
out-of-sample and few-shots setting.