There has been a growing interest in capturing and maintaining causal
relationships in Neural Network (NN) models in recent years. We study causal
approaches to estimate and maintain input-output attributions in NN models in
this work. In particular, existing efforts in this direction assume
independence among input variables (by virtue of the NN architecture), and
hence study only direct causal effects. Viewing an NN as a structural causal
model (SCM), we instead focus on going beyond direct effects, introduce edges
among input features, and provide a simple yet effective methodology to capture
and maintain direct and indirect causal effects while training an NN model. We
also propose effective approximation strategies to quantify causal attributions
in high dimensional data. Our wide range of experiments on synthetic and
real-world datasets show that the proposed ante-hoc method learns causal
attributions for both direct and indirect causal effects close to the ground
truth effects.

本文研究了神经网络中因果关系的估计及维护，提出了一种有效的方法来捕获和维护直接和间接的因果关系，并在高维数据中量化因果属性。通过实验验证此方法学习的因果属性能够接近于真实效果。