Causal reasoning is the main learning and explanation tool used by humans. AI systems should possess causal reasoning capabilities to be deployed in the real world with trust and reliability. Introducing the ideas of causality to machine learning helps in providing better learning and explainable models. Explainability, causal disentanglement are some important aspects of any machine learning model. Causal explanations are required to believe in a model's decision and causal disentanglement learning is important for transfer learning applications. We exploit the ideas of causality to be used in deep learning models to achieve better and causally explainable models that are useful in fairness, disentangled representation, etc.

中文翻译：

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神经网络中的因果关系——扩展摘要

因果推理是人类使用的主要学习和解释工具。人工智能系统应该具有因果推理能力，以信任和可靠的方式部署在现实世界中。将因果关系的概念引入机器学习有助于提供更好的学习和可解释的模型。可解释性、因果关系是任何机器学习模型的一些重要方面。需要因果解释来相信模型的决定，而因果解开学习对于迁移学习应用很重要。我们利用深度学习模型中使用的因果关系的思想来实现更好的、因果可解释的模型，这些模型在公平性、解开表示等方面很有用。