🤖 AI Summary
Existing causal discovery methods struggle to simultaneously achieve high accuracy and interpretability, particularly under complex, noisy data conditions. To address this, we propose REX—the first method to deeply integrate Shapley values into a machine learning–driven causal graph learning framework, unifying causal structure identification with quantitative, attribution-based interpretation of individual causal edges. REX synergizes neural network modeling capacity with the local interpretability of Shapley values, supporting both nonlinear functional relationships and additive noise models; it further enhances robustness via edge significance assessment. Experiments demonstrate that REX consistently outperforms state-of-the-art methods on synthetic benchmarks. On the Sachs single-cell protein signaling dataset, REX achieves an accuracy of 0.952 with zero false-positive edges—marking substantial improvements in both reliability and interpretability of causal discovery.
📝 Abstract
Explainability 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 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 REX, 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 REX outperforms state-of-the-art causal discovery methods across diverse data generation processes, including non-linear and additive noise models. Moreover, REX 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 REX'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, REX bridges the gap between predictive modeling and causal inference, offering an effective tool for understanding complex causal structures. REX is publicly available at https://github.com/renero/causalgraph.