Boosting Causal Structure Learning: An Asymmetric Exponential Modulation Gaussian-Based Adaptive Sample Reweighting Framework

Recent advances in differentiable score-based methods for Directed Acyclic Graph (DAG) structure learning have revolutionized the problem of combinatorial structure learning, transforming it into a continuous optimization task. Despite their remarkable success, these methods rely on a key assumption that all samples have the same level of difficulty and no data heterogeneity. When this assumption does not hold, causal discovery algorithms based on it inevitably return networks with many spurious edges. Despite existing research, the current method ignores the reality of outliers in the samples, introducing certain limitations that still result in erroneous edges. Inspired by the rapid decay of the Gaussian distribution as distance from the center increases, we propose an innovative adaptive sample reweighting framework based on asymmetric exponential modulation Gaussian, coined DAG-AEG. DAG-AEG boosts DAG structure learning by analyzing the distribution of sample losses and employing the proposed method for adaptive sample attention. Additionally, it can be adapted to heterogeneous data. We used various causal structure learning methods to test the performance of DAG-AEG on synthetic and real datasets. The experimental results demonstrate that the proposed framework significantly improves the performance across all methods, outperforming existing methods.