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，但其是否习得任何相关因果关系尚不明确，同时其黑箱特性使得建模者难以理解和调试模型。我们提出一种创新方法来解决这些问题，该方法允许双向交互：神经网络赋能的机器可展现其习得的底层因果图，而人类可通过修改因果图后重新注入机器的方式对机器提出争议。学习模型被保证符合因果图并遵循专家知识——部分知识亦可预先输入。通过构建模型行为的窗口并支持知识注入，我们的方法使实践者能基于从数据中发现的并支撑预测的因果结构来调试网络。基于真实与合成表格数据的实验表明，与最先进的正则化网络相比，我们的方法在生成简洁网络（输入层规模缩小达7倍）的同时，预测性能提升最高达2.4倍。