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：近来，学界对学习与解释神经网络（NN）模型中因果效应的兴趣日益增长。受制于神经网络架构的特性，以往的方法仅考虑输入变量间相互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（SCM），并通过在输入神经元之间引入前馈连接，将研究范畴拓展至包含间接因果效应。我们提出一种先验方法，能够在神经网络模型训练过程中捕获并维持直接、间接及总因果效应。同时，我们提出一种量化神经网络模型中已学习因果效应的算法，以及针对高维数据中因果效应量化的高效近似策略。在合成数据集与真实世界数据集上开展的大量实验表明，与现有方法相比，我们提出的先验方法所学习的因果效应能更准确地逼近真实因果效应。