Robust feature selection is vital for creating reliable and interpretable Machine Learning (ML) models. When designing statistical prediction models in cases where domain knowledge is limited and underlying interactions are unknown, choosing the optimal set of features is often difficult. To mitigate this issue, we introduce a Multidata (M) causal feature selection approach that simultaneously processes an ensemble of time series datasets and produces a single set of causal drivers. This approach uses the causal discovery algorithms PC1 or PCMCI that are implemented in the Tigramite Python package. These algorithms utilize conditional independence tests to infer parts of the causal graph. Our causal feature selection approach filters out causally-spurious links before passing the remaining causal features as inputs to ML models (Multiple linear regression, Random Forest) that predict the targets. We apply our framework to the statistical intensity prediction of Western Pacific Tropical Cyclones (TC), for which it is often difficult to accurately choose drivers and their dimensionality reduction (time lags, vertical levels, and area-averaging). Using more stringent significance thresholds in the conditional independence tests helps eliminate spurious causal relationships, thus helping the ML model generalize better to unseen TC cases. M-PC1 with a reduced number of features outperforms M-PCMCI, non-causal ML, and other feature selection methods (lagged correlation, random), even slightly outperforming feature selection based on eXplainable Artificial Intelligence. The optimal causal drivers obtained from our causal feature selection help improve our understanding of underlying relationships and suggest new potential drivers of TC intensification.

翻译：稳健的特征选择对于构建可靠且可解释的机器学习模型至关重要。在领域知识有限且底层交互未知的情况下设计统计预测模型时，选择最优特征集通常存在困难。为解决此问题，我们提出了一种多数据因果特征选择方法，该方法可同时处理一组时间序列数据集并生成单一因果驱动因子集。该方法采用Tigramite Python包中实现的PC1或PCMCI因果发现算法，这些算法利用条件独立性检验推断部分因果图。我们的因果特征选择方法在将剩余因果特征作为输入传递给预测目标的机器学习模型（多元线性回归、随机森林）之前，会过滤掉因果上的虚假连接。我们将该框架应用于西太平洋热带气旋的统计强度预测，此类任务中通常难以准确选择驱动因子及其降维方式（时间滞后、垂直层及区域平均）。在条件独立性检验中使用更严格的显著性阈值有助于消除虚假因果关系，从而帮助机器学习模型更好地泛化至未见过的热带气旋案例。基于简化特征集的M-PC1方法在性能上优于M-PCMCI、非因果机器学习及其他特征选择方法（滞后相关、随机），甚至略优于基于可解释人工智能的特征选择。通过因果特征选择获得的最优因果驱动因子有助于加深对底层关系的理解，并揭示热带气旋增强的潜在新驱动因子。