Recently, there has been a growing interest in learning and explaining causal effects within Neural Network (NN) models. By virtue of NN architectures, previous approaches consider only direct and total causal effects assuming independence among input variables. We view an NN as a structural causal model (SCM) and extend our focus to include indirect causal effects by introducing feedforward connections among input neurons. We propose an ante-hoc method that captures and maintains direct, indirect, and total causal effects during NN model training. We also propose an algorithm for quantifying learned causal effects in an NN model and efficient approximation strategies for quantifying causal effects in high-dimensional data. Extensive experiments conducted on synthetic and real-world datasets demonstrate that the causal effects learned by our ante-hoc method better approximate the ground truth effects compared to existing methods.

翻译：近期，在神经网络模型中学习与解释因果效应的研究兴趣日益增长。由于神经网络架构的特性，先前的方法仅考虑输入变量独立假设下的直接因果效应与总因果效应。本文将神经网络视为结构因果模型，通过引入输入神经元之间的前馈连接，将研究范围扩展至间接因果效应。我们提出一种先验方法，在神经网络模型训练过程中捕获并维持直接、间接与总因果效应。同时，我们设计了量化神经网络模型中已学习因果效应的算法，以及针对高维数据因果效应量化的高效近似策略。在合成数据集与真实数据集上的大量实验表明，与现有方法相比，本文先验方法所学习的因果效应更接近真实因果效应。