  Causal inference is the process of capturing cause-effect relationship among
variables. Most existing works focus on dealing with structured data, while
mining causal relationship among factors from unstructured data, like text, has
been less examined, but is of great importance, especially in the legal domain.
  In this paper, we propose a novel Graph-based Causal Inference (GCI)
framework, which builds causal graphs from fact descriptions without much human
involvement and enables causal inference to facilitate legal practitioners to
make proper decisions. We evaluate the framework on a challenging similar
charge disambiguation task. Experimental results show that GCI can capture the
nuance from fact descriptions among multiple confusing charges and provide
explainable discrimination, especially in few-shot settings. We also observe
that the causal knowledge contained in GCI can be effectively injected into
powerful neural networks for better performance and interpretability.

Computation and Language;Machine Learning