Legal judgment Prediction (LJP), aiming to predict a judgment based on fact descriptions, serves as legal assistance to mitigate the great work burden of limited legal practitioners. Most existing methods apply various large-scale pre-trained language models (PLMs) finetuned in LJP tasks to obtain consistent improvements. However, we discover the fact that the state-of-the-art (SOTA) model makes judgment predictions according to wrong (or non-casual) information, which not only weakens the model's generalization capability but also results in severe social problems like discrimination. Here, we analyze the causal mechanism misleading the LJP model to learn the spurious correlations, and then propose a framework to guide the model to learn the underlying causality knowledge in the legal texts. Specifically, we first perform open information extraction (OIE) to refine the text having a high proportion of causal information, according to which we generate a new set of data. Then, we design a model learning the weights of the refined data and the raw data for LJP model training. The extensive experimental results show that our model is more generalizable and robust than the baselines and achieves a new SOTA performance on two commonly used legal-specific datasets.

本文研究法律判决预测（LJP），提出了一种基于因果知识的框架，用于引导模型从法律文本中学习潜在的因果关系，通过开放式信息提取（OIE）筛选出高比例的因果信息，并使用模型学习精炼数据的权重和原始数据，使模型具有更好的泛化性和鲁棒性。经过广泛的实验验证，该模型比基线模型更多样化且在两个常用的法律特定数据集上实现了新的 SOTA 性能。

知识即力量：理解因果关系使法律判断预测模型更具普适性和健壮性