Estimating pairwise interaction effects, i.e., the difference between the joint effect and the sum of marginal effects of two input features, with uncertainty properly quantified, is centrally important in science applications. We propose a non-parametric probabilistic method for detecting interaction effects of unknown form. First, the relationship between the features and the output is modelled using a Bayesian neural network, leveraging on the representation capability of deep neural networks. Second, interaction effects and their uncertainty are estimated from the trained model. For the second step we propose a simple and intuitive global interaction measure: Expected Integrated Hessian (EIH), whose uncertainty can be estimated using the predictive uncertainty. Two important properties of the Bayesian EIH are: 1. interaction estimation error is upper bounded by the prediction error of the neural network, which ensures interaction detection can be improved by training a more accurate model; 2. uncertainty of the Bayesian EIH is well-calibrated provided the prediction uncertainty is calibrated, which is easier to test and guarantee. The method outperforms the available alternatives on simulated and real-world data, and we demonstrate its ability to detect interpretable interactions also between higher-level features (at deeper layers of the neural network).
翻译:在科学应用中,具有不确定性的两个输入特征的共同效应和边际效应之差与边际效应之和之间的差别是最重要的。我们建议采用非参数性概率法来探测未知形态的相互作用效应。首先,这些特征与输出之间的关系是利用贝叶斯神经网络的模拟模型,利用深神经网络的显示能力。第二,从经过培训的模型中估算出互动效应及其不确定性。第二步,我们提出一个简单和直观的全球互动计量:预期的综合赫森(EIH),其不确定性可以用预测不确定性来估计。Bayesian EIH的两个重要属性是:1. 互动估计错误被神经网络的预测错误所覆盖,通过培训一个更精确的模型可以确保改进互动检测; 2. 贝叶斯环境网络的不确定性得到很好的校准,条件是对预测不确定性进行校准,这比较容易测试和保证。该方法超越了在模拟和现实世界深度数据上的现有替代方法(我们还演示其测测测深层数据的能力)。