关键性能指标（KPI）的软测量在复杂工业过程决策中起着至关重要的作用。许多研究人员已经使用先进的机器学习（ML）或深度学习（DL）模型开发出了数据驱动的软传感器。其中，特征选择是一个关键的问题，因为一个原始的工业数据集通常是高维的，并不是所有的特征都有利于软传感器的建立。一种完善的特征选择方法不应该依赖于超参数和后续的ML或DL模型。换言之，这种特征选择方法应该能够自动地选择一个特征子集进行软传感器建模，选择的每个特征对工业KPI 都有独特的因果影响。因此，本研究提出了一种受因果模型启发的自动特征选择方法，用于工业KPI 的软测量。首先，受后非线性因果模型的启发，本研究将数据驱动的软传感器与信息论相结合，以量化原始工业数据集中每个特征和KPI之间的因果效应。然后，提出了一种新的特征选择方法，即自动选择具有非零因果效应的特征来构造特征子集。最后，利用所构造的子集，通过AdaBoost 集成策略开发KPI 的软传感器。两个实际工业应用中的实验证实了该方法的有效性。在未来，该方法也可以应用于其他工业过程，以帮助开发更先进的数据驱动的软传感器。

The soft sensing of key performance indicators (KPIs) plays an essential role in the decision-making of complex industrial processes. Many researchers have developed data-driven soft sensors using cutting-edge machine learning (ML) or deep learning (DL) models. Moreover, feature selection is a crucial issue because a raw industrial dataset is usually high-dimensional, and not all features are conducive to the development of soft sensors. A perfect feature-selection method should not rely on hyperparameters and subsequent ML or DL models. Rather, it should be able to automatically select a subset of features for soft sensor modeling, in which each feature has a unique causal effect on industrial KPIs. Therefore, this study proposes a causal model-inspired automatic feature-selection method for the soft sensing of industrial KPIs. First, inspired by the post-nonlinear causal model, we integrate it with information theory to quantify the causal effect between each feature and the KPIs in the raw industrial dataset. After that, a novel feature-selection method is proposed to automatically select the feature with a non-zero causal effect to construct the subset of features. Finally, the constructed subset is used to develop soft sensors for the KPIs by means of an AdaBoost ensemble strategy. Experiments on two practical industrial applications confirm the effectiveness of the proposed method. In the future, this method can also be applied to other industrial processes to help develop more advanced data-driven soft sensors.