Everything Has a Cause: Leveraging Causal Inference in Legal Text Analysis
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.
Code (1)
Tasks
Causal InferenceSimilar Papers 제목 키워드 기반
FAIR: A Causal Framework for Accurately Inferring Judgments Reversals
Artificial intelligence researchers have made significant advances in legal intelligence in recent years. However, the existing studies have not focused on the important value embedded in judgments reversals, which limit…
Causal InferenceProbabilistic Modelling is Sufficient for Causal Inference
Causal inference is a key research area in machine learning, yet confusion reigns over the tools needed to tackle it. There are prevalent claims in the machine learning literature that you need a bespoke causal framework…
Causal InferenceLLM-Assisted Causal Structure Disambiguation and Factor Extraction for Legal Judgment Prediction
Mainstream methods for Legal Judgment Prediction (LJP) based on Pre-trained Language Models (PLMs) heavily rely on the statistical correlation between case facts and judgment results. This paradigm lacks explicit modelin…
Causal InferenceLeveraging directed causal discovery to detect latent common causes
The discovery of causal relationships is a fundamental problem in science and medicine. In recent years, many elegant approaches to discovering causal relationships between two variables from observational data have been…
Causal DiscoveryCausal InferenceDebiasing Alternative Data for Credit Underwriting Using Causal Inference
Alternative data provides valuable insights for lenders to evaluate a borrower's creditworthiness, which could help expand credit access to underserved groups and lower costs for borrowers. But some forms of alternative …
Causal Inference