Everything Has a Cause: Leveraging Causal Inference in Legal Text Analysis
This addresses the challenge of causal inference in legal text analysis for legal practitioners, but it is incremental as it adapts causal methods to a specific domain.
The paper tackled the problem of mining causal relationships from unstructured legal text to disambiguate similar charges, and the result was a Graph-based Causal Inference framework that provided explainable discrimination, especially in few-shot settings.
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.