rootjust

Rootjust is a framework for extracting minimal root cause justifications from causal graphs and rule-based systems. It aims to identify the smallest, sufficient set of root causes that justify a particular outcome, enabling concise explanations for decision-making processes.

Origin and terminology: The term emerged in explainable AI research in the late 2010s and early 2020s as researchers sought to formalize root-cause explanations separate from intermediate factors. It treats root nodes as primary causes and distinguishes them from variables used to derive or constrain outcomes.

Key concepts: A root set is a subset of root causes that, under a given model, is sufficient to explain the outcome. Justification refers to the explanatory link between the root set and the conclusion. Minimality requires that no proper subset of the root set is sufficient. The approach emphasizes parsimonious, auditable explanations suitable for human review and regulatory needs.

Algorithms and methods: Greedy extraction iteratively adds root causes by their marginal contribution until the outcome is guaranteed. Minimal hitting set methods search candidate root sets that cover all necessary justification paths and select the smallest. SAT-based methods translate the model into a logical formula and minimize the portion of the assignment corresponding to root elements. Practical implementations often rely on Bayesian networks or rule engines.

Applications: Rootjust is applied in explainable AI, risk assessment and compliance, medical decision support, and software debugging, where concise root-level explanations support transparency, auditability, and user trust.

Implementation and availability: Research prototypes and open-source libraries exist, typically interoperating with existing causal graphs and rule systems. There is no universal standard, and approaches vary by domain and model type.

See also: Explainable AI, causal inference, root cause analysis, minimal sufficient causes.