# Everything Has a Cause: Leveraging Causal Inference in Legal Text Analysis

arXiv: https://arxiv.org/abs/2104.09420 · HTML: https://mlsanity.com/paper/2104.09420/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 47 references that are in the map.
- First posted on arXiv 19 Apr 2021.

## Builds on (most cited first, 15 of 15)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Causal Effect Inference with Deep Latent-Variable Models](https://mlsanity.com/paper/1705.08821/) (2017)
- [CAIL2018: A Large-Scale Legal Dataset for Judgment Prediction](https://mlsanity.com/paper/1807.02478/) (2018)
- [Causal Inference in the Presence of Latent Variables and Selection Bias](https://mlsanity.com/paper/1302.4983/) (2013)
- [Reducing Gender Bias in Abusive Language Detection](https://mlsanity.com/paper/1808.07231/) (2018)
- [Joint Reasoning for Temporal and Causal Relations](https://mlsanity.com/paper/1906.04941/) (2019)
- [Distinguish Confusing Law Articles for Legal Judgment Prediction](https://mlsanity.com/paper/2004.02557/) (2020)
- [Learning to Predict Charges for Criminal Cases with Legal Basis](https://mlsanity.com/paper/1707.09168/) (2017)
- [How to Make Causal Inferences Using Texts](https://mlsanity.com/paper/1802.02163/) (2018)
- [Text and Causal Inference: A Review of Using Text to Remove Confounding from Causal Estimates](https://mlsanity.com/paper/2005.00649/) (2020)
- [A causal framework for explaining the predictions of black-box sequence-to-sequence models](https://mlsanity.com/paper/1707.01943/) (2017)
- [Challenges of Using Text Classifiers for Causal Inference](https://mlsanity.com/paper/1810.00956/) (2018)
- [Causal Inference of Script Knowledge](https://mlsanity.com/paper/2004.01174/) (2020)
- [Transfer Learning for Estimating Causal Effects using Neural Networks](https://mlsanity.com/paper/1808.07804/) (2018)
- [Adapting Text Embeddings for Causal Inference](https://mlsanity.com/paper/1905.12741/) (2019)

## Built on by (most cited first, 14 of 14)

- [A Survey of Knowledge-Intensive NLP with Pre-Trained Language Models](https://mlsanity.com/paper/2202.08772/) (2022)
- [Interpreting the Robustness of Neural NLP Models to Textual Perturbations](https://mlsanity.com/paper/2110.07159/) (2021)
- [Bringing order into the realm of Transformer-based language models for artificial intelligence and law](https://mlsanity.com/paper/2308.05502/) (2023)
- [Discovering and Reasoning of Causality in the Hidden World with Large Language Models](https://mlsanity.com/paper/2402.03941/) (2024)
- [Prototype-Based Interpretability for Legal Citation Prediction](https://mlsanity.com/paper/2305.16490/) (2023)
- [Logic Rules as Explanations for Legal Case Retrieval](https://mlsanity.com/paper/2403.01457/) (2024)
- [JUREX-4E: Juridical Expert-Annotated Four-Element Knowledge Base for Legal Reasoning](https://mlsanity.com/paper/2502.17166/) (2025)
- [Do Charge Prediction Models Learn Legal Theory?](https://mlsanity.com/paper/2210.17108/) (2022)
- [Validity Assessment of Legal Will Statements as Natural Language Inference](https://mlsanity.com/paper/2210.16989/) (2022)
- [FAIR: A Causal Framework for Accurately Inferring Judgments Reversals](https://mlsanity.com/paper/2306.11585/) (2023)
- [MCNS: Mining Causal Natural Structures Inside Time Series via A Novel Internal Causality Scheme](https://mlsanity.com/paper/2309.06739/) (2023)
- [Incorporating Legal Logic into Deep Learning: An Intelligent Approach to Probation Prediction](https://mlsanity.com/paper/2508.12286/) (2025)
- [LLM-Assisted Causal Structure Disambiguation and Factor Extraction for Legal Judgment Prediction](https://mlsanity.com/paper/2603.11446/) (2026)
- [LePREC: Reasoning as Classification over Structured Factors for Assessing Relevance of Legal Issues](https://mlsanity.com/paper/2604.19464/) (2026)

## Abstract (arXiv)

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.