## 1 Introduction  
In order to reduce the computational cost of model classification or regression, it is desirable to select as few features as possible while ensuring estimation quality. Furthermore, utilizing all available features not only invalidates the model's calculation [1] but also increases the likelihood of overfitting [2], consequently reducing the predictive accuracy. Feature selection is a fundamental and relatively straightforward strategy in data mining [3]. It should be noted that we focus solely on the correlation between variables, not causality, as correlation does not imply causation. Correlations can exist between two non-causal variables [4]. The traditional feature selection algorithm searches for the relevant feature according to the correlation between feature variables and target variable [5]. However, correlation can only indicate the coexistence relationship between the target variable and a feature, and does not account for the underlying mechanisms that influence the target variable [6]. For instance, consider \"lung cancer\" as the target variable, and \"yellow fingers\" and \"smoking\" as the feature variables. \"Smoking\" can serve as an explanatory factor for \"lung cancer,\" and the long-term habit of smoking leads to tar pollution on fingers. While a correlation exists between \"yellow fingers,\" \"smoking,\" and \"lung cancer,\" only \"smoking\" and \"lung cancer\" exhibit a causal relationship. If some smokers attempt to conceal their smoking habits by removing the yellow stains from their fingers, a prediction model relying on \"yellow fingers\" would be ineffective, whereas a model based on \"smoking\" would be more reliable. These studies and practices demonstrate that causality unveils the genuine impact of one variable on another, and models based on causality offer enhanced explanatory power and robustness [7]. Currently, causality has found successful applications in various domains such as medicine, economy, environment, interpretable artificial intelligence, and more [8]. Consequently, we made the decision to incorporate causality into feature selection and identify features by establishing a causal structure diagram between the features and target variables. The NOTEARS algorithm [9], utilized in this study, distinguishes itself from prior causal discovery algorithms that rely on local heuristics. It does not require an extensive understanding of graph theory; instead, it can identify the globally optimal causal graph structure by transforming the initially intricate structural learning algorithm into a relatively manageable numerical optimization problem. Utilizing the acquired global causal graph structure, this study calculates the causal strength among the characteristic nodes and subsequently obtains a feature subset through the process of feature selection. This study utilizes the Feature Selection algorithm based on the NOTEARS causal discovery algorithm (FSNT) to investigate the causal relationship among data feature variables. It extracts informative features from multidimensional feature data, generates a causal network based on these features to visually represent the causal relationship [10], and employs the causal strength between nodes for feature selection [11]. To validate the efficacy of the proposed method, commonly employed feature selection methods serve as benchmark algorithms and experiments are conducted on publicly available real datasets. The selected benchmark algorithms comprise the XGBoost Feature Selection (XGBFS), the Chi-square Filter Feature Selection (CSFFS) [12], and the Random Forest-based Hybrid Feature Selection (RFFHS). Additionally, a Baseline is established by considering no feature selection. Four comparison algorithms are employed, along with a diverse range of popular classification and regression algorithms, to compare the performance on six real datasets. This comparison aims to ascertain the strengths and weaknesses of utilizing the FSNT algorithm.