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.

本论文提出了一种新颖的基于图的因果推理（GCI）框架，可以从事实描述中构建因果图，以帮助法律从业人员做出适当的决策，并且该框架中的因果知识可以有效地注入强大的神经网络以获得更好的性能和可解释性。