Can Causal Discovery Algorithms Help in Generating Legal Arguments?

arXiv stat.ML / 5/5/2026

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Key Points

The paper explores whether causal discovery algorithms—developed from Judea Pearl’s work on probabilistic and causal reasoning—can be used to automate parts of legal argument generation.
It creates a new legal dataset by defining 17 legal concepts (e.g., physical assault, property dispute) and annotating 150 homicide cases with these concepts.
Using several widely used causal discovery algorithms on the annotated data, the study identifies causal relationships among legal concepts and assigns quantified belief levels as probabilities.
The results suggest that certain discovered causal links can produce viable legal arguments; for example, showing physical assault did not occur can imply (with probability 1) that the homicide was not committed due to a property-related dispute.
Overall, the work argues that causal discovery methods may open promising avenues for future research on automated legal reasoning.

Abstract

In 2011, Judea Pearl received the Turing Award, considered the Nobel Prize in Computing, for fundamental contributions to artificial intelligence through the development of a calculus for probabilistic and causal reasoning. It includes pioneering the development of causal discovery algorithms. These computer algorithms can analyze large multivariate datasets and automatically discover the causal relationships among the constituent variables. They have been widely used in many critical fields such as medicine and economics to support decisions. However, to our knowledge, they have not been leveraged in law. This paper attempts to alleviate this gap by investigating whether causal discovery algorithms can be leveraged for automated generation of legal arguments. To that end, a novel legal dataset is prepared by identifying 17 legal concepts, such as physical assault and property dispute. A curated collection of 150 homicide cases are annotated with these concepts, e.g., a case is annotated with physical assault only if a physical assault had been reported in that case. Subsequently, a selected set of widely-used causal discovery algorithms is applied to the annotated dataset to discover the causal relationships between the legal concepts. Additionally, the degrees of belief associated with the discovered relationships are quantified in mathematical probabilities. It is shown that some of the causal relationships help generate viable legal arguments, e.g., if one could establish that a physical assault has not taken place during a homicide, it should be a sufficient condition (with probability 1) to establish that the homicide has not been committed due to a property-related dispute. Thus, this paper shows that causal discovery algorithms can be helpful in generating legal arguments, opening up avenues for promising future endeavors.