Causal Machine Learning for Discovering Actionable Insights in Observational Data
TL;DR
A unified causal machine learning framework that integrates structural causal modeling with data-driven estimation techniques to enable robust causal discovery and effect estimation and achieves higher causal discovery accuracy with improved precision and recall of causal edges.
Abstract
Traditional machine learning models achieve strong predictive performance but often are unable to reliably uncover causal relationships required for reliable decision-making, particularly in observational data where controlled experiments are not feasible. This limitation creates a critical gap between prediction and actionable insight, as correlation-based models are vulnerable to confounding bias and poor generalization under distributional shifts. To address this challenge, this study proposes a unified causal machine learning framework that integrates structural causal modeling with data-driven estimation techniques to enable robust causal discovery and effect estimation. The methodology combines hybrid causal structure learning (constraint-based and score-based approaches) with advanced causal effect estimation methods, including propensity score techniques and doubly robust estimators. The framework is evaluated on both synthetic datasets with known causal structures and real-world datasets to assess its accuracy, robustness, and interpretability. Experiments are conducted using multiple runs with controlled settings to ensure reproducibility and statistical validity. The results demonstrate that the proposed framework significantly outperforms traditional predictive models and standalone causal methods. It achieves higher causal discovery accuracy with improved precision and recall of causal edges, reduces estimation error in Average Treatment Effect (ATE), and maintains stable predictive performance under distributional shifts. Statistical analysis confirms significant improvements (p < 0.01) with large effect sizes, indicating strong reliability and robustness. This research aims to bridge the gap between prediction and explanation by enabling machine learning systems to generate actionable, interpretable, and causally valid insights. The findings highlight the importance of integrating causal reasoning into data science workflows to support informed decision-making, intervention planning, and trustworthy AI development.