面向可争议神经网络的因果发现与知识注入（含附录）
Causal Discovery and Knowledge Injection for Contestable Neural Networks (with Appendices)
AI总结：
提出一种支持人机双向交互的方法，允许机器暴露学习到的因果图供人类修改并重新注入，从而实现知识注入与网络调试，在表格数据上预测性能提升最高2.4倍且网络更简洁。
摘要：
神经网络已被证明在解决机器学习任务方面非常有效，但尚不清楚它们是否学到了任何相关的因果关系，同时其黑盒特性使得建模者难以理解和调试它们。我们提出了一种新方法来克服这些问题，该方法允许双向交互：由神经网络驱动的机器可以暴露其学习到的底层因果图，而人类可以通过修改因果图来质疑机器，然后再将其重新注入机器中。学习到的模型保证符合这些图并遵循专家知识，其中部分专家知识也可以预先提供。通过为模型行为建立一扇窗口并实现知识注入，我们的方法允许从业者基于从数据中发现并支撑预测的因果结构来调试网络。在真实和合成表格数据上的实验表明，与SOTA正则化网络相比，我们的方法将预测性能提高了最多2.4倍，同时生成了简洁的网络，其输入层最多小7倍。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
