Neural networks have proven to be effective at solving machine learning tasks but it is unclear whether they learn any relevant causal relationships, while their black-box nature makes it difficult for modellers to understand and debug them. We propose a novel method overcoming these issues by allowing a two-way interaction whereby neural-network-empowered machines can expose the underpinning learnt causal graphs and humans can contest the machines by modifying the causal graphs before re-injecting them into the machines. The learnt models are guaranteed to conform to the graphs and adhere to expert knowledge, some of which can also be given up-front. By building a window into the model behaviour and enabling knowledge injection, our method allows practitioners to debug networks based on the causal structure discovered from the data and underpinning the predictions. Experiments with real and synthetic tabular data show that our method improves predictive performance up to 2.4x while producing parsimonious networks, up to 7x smaller in the input layer, compared to SOTA regularised networks.

翻译：神经网络已被证明能有效解决机器学习任务，但尚不清楚它们是否学习到任何相关的因果关系，而其黑箱特性使得建模者难以理解和调试这些网络。我们提出一种新方法，通过允许双向交互来克服这些问题：基于神经网络的机器能够展示所学的底层因果图，而人类可以通过修改因果图后将其重新注入机器来对机器提出异议。学习到的模型保证符合因果图并遵循专家知识，其中部分知识也可预先提供。通过构建模型行为的窗口并实现知识注入，我们的方法使实践者能够基于从数据中发现并支撑预测的因果结构来调试网络。在真实与合成表格数据上的实验表明，与当前最优的正则化网络相比，我们的方法可将预测性能提升至2.4倍，同时生成简约网络——输入层体积缩小多达7倍。