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.

翻译：原因推理是人类使用的主要学习和解释工具。 人工智能系统应具备在现实世界中以信任和可靠的方式部署的因果推理能力。 将因果推理理念引入机器学习有助于提供更好的学习和可解释的模式。 任何机器学习模式的一些重要方面都是可解释性、因果解析。 需要由因果推理来相信模型的决定,而因果解析学习对于转移学习应用非常重要。 我们利用因果关系理念在深层学习模型中应用,以获得更完善和因果解析的模型,这些模型对公平、分解的代表性等非常有用。