ReX: Causal discovery based on machine learning and explainability techniques

• A causal discovery method is introduced based on machine learning and explainability techniques. • Complex relationships in the data are captured and approximated by ML models, echoing their structural causal model. • Shapley values are used to help measuring how each feature contributes to data’s causal structure. • Results comparable to state-of-the-art methods in synthetic data and Sachs protein network support the approach. • A repository with open-source code is provided to foster wide adoption and further development. Explainable Artificial Intelligence (XAI) techniques hold significant potential for enhancing the causal discovery process, which is crucial for understanding complex systems in areas like healthcare, economics, and artificial intelligence. However, no causal discovery methods currently incorporate explainability into their models to derive the causal graphs. Thus, in this paper we explore this innovative approach, as it offers substantial potential and represents a promising new direction worth investigating. Specifically, we introduce R e X, a causal discovery method that leverages machine learning (ML) models coupled with explainability techniques, specifically Shapley values, to identify and interpret significant causal relationships among variables. Comparative evaluations on synthetic datasets comprising continuous tabular data reveal that R e X outperforms state-of-the-art causal discovery methods across diverse data generation processes, including non-linear and additive noise models. Moreover, R e X was tested on the Sachs single-cell protein-signaling dataset, achieving a precision of 0.952 and recovering key causal relationships with no incorrect edges. Taking together, these results showcase R e X’s effectiveness in accurately recovering true causal structures while minimizing false positive predictions, its robustness across diverse datasets, and its applicability to real-world problems. By combining ML and explainability techniques with causal discovery, R e X bridges the gap between predictive modeling and causal inference, offering an effective tool for understanding complex causal structures. R e X is publicly available at https://github.com/renero/causalgraph .