Causal-Nest: A framework for automated causal discovery and inference

Causal discovery offers a path for Machine Learning models to move beyond mere correlation to understand cause-and-effect relationships. However, its operational complexity remains a significant barrier for non-expert developers. This paper introduces Causal-Nest , a new, comprehensive framework that simplifies and operationalizes the causal analysis pipeline for non-experts. The contribution of the framework is demonstrated through an extensive, two-part evaluation. First, we validate its theoretical correctness and diagnostic power using the well-known Sachs et al. dataset and an extended benchmark of seven real-world datasets with known causal constraints. Our results show that the proposed framework effectively assesses causal graph integrity, using metrics, including the novel Knowledge Integrity Score (KIS), to pinpoint structural discovery failures and statistical unreliability quantitatively. Second, we demonstrate its practical utility in a common Machine Learning scenario where no such ground truth is available: telecommunications customer churn prediction. We show that using the proposed framework to guide a causality-driven feature engineering process yields a causal oversampled feature set that significantly improves the minority-class F1-Score of ensemble prediction models (e.g., CBC, GBMC). This work validates Causal-Nest as a single, dual-purpose tool that successfully bridges the gap between rigorous causal validation and practical, performance-driven machine learning applications. • Introduces Causal-Nest , a framework to simplify practical causal discovery. • Integrates 12 causal discovery algorithms with parallel execution. • Introduces Knowledge Integrity Score (KIS) and Priority Score metrics for model validation and prioritization. • Supports causal effect estimation and refutation with time limits. • Enhances ML feature engineering through causality-driven insights, validated by improved churn prediction metrics in a case study.