Causality in Machine Learning

机器学习中的因果关系

• 批准号: RGPIN-2022-03667
• 负责人: Matwin, Stan
• 金额: $ 2.11万
• 依托单位: Dalhousie University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=750275
• 关键词: Causality; Machine; Learning

Causality in Machine Learning is often understood as the ability to understand decisions provided by a machine-learning model in terms of the knowledge of the domain in which the model operates, and the ability to reason about such decisions. A causal model has an "introspective" ability to reason about itself. Learning a causal model is a much more difficult task than the one performed by current Machine Learning methods, including Deep Learning, that determine a "correlational" or "pattern-matching" relationship between the inputs of the model and its decision. I propose here a research program in causality in Machine Learning. Causality is one of the main challenges before the field of Machine Learning. Moreover, having a causal representation of a model will allow a progress of Machine Learning towards the abilities of human intelligence, such as learning with a few examples. I propose to connect with the rich body of existing Artificial Intelligence work exploring the use of logic to reason about causes of changing states of the world and variables describing the world. The proposed research program is founded on my previous work. In particular, my active participation in a sub-area of Machine Learning known as Inductive Logic Programming will be useful. I propose to interpret models obtained with Deep Learning using logic. Use of Inductive Logic Programming will enable us to build models behaving similarly to models obtained by Deep Learning. These ILP models will be "distilled" from the Deep Learning models, and will be expressed as rules in first-order logic. This will make them interpretable by humans. It will also facilitate integrating previous knowledge expressed in logic with the learned models. Even partial success of research on causality is likely to have significant impact. Causality is necessary for broader social acceptance of models developed using Machine Learning for decision-making concerning humans. For instance, the European Union GDPR directive stipulates that any such model should be explainable, i.e. a person about whom the model has made a decision should be able to obtain an explanation of the model's decision understandable to them. Understanding models will eventually allow us to avoid models that make decisions about humans based on gender, ethnicity, etc. For example, the group in Pisa with which I collaborate has access to claim processing data of one of the leading Italian insurance companies. We will look at the explainability of decisions taken by their automated insurance clam processing systems. Addressing causality is a huge challenge. In this program I propose to make inroads into distillation of Deep Learning models into understandable models that also make causality explicit, and in being able to assign multiple factors as combined causes of a given effect predicted by a model. I will also young researchers who will continue the important work in causality for Machine Learning.

机器学习中的因果关系通常被理解为理解机器学习模型提供的决策的能力，以了解模型运行的领域的知识以及对此类决策进行推理的能力。因果模型具有“内省”自我推理的能力。学习因果模型比当前的机器学习方法（包括深度学习）确定模型的输入与其决策之间的“相关”或“模式匹配”关系的任务要困难得多。我在这里提出了一项关于机器学习因果关系的研究计划。因果关系是机器学习领域之前的主要挑战之一。此外，拥有一个模型的因果代表将使机器学习朝着人类智能的能力取得进步，例如学习一些例子。我建议与现有的人工智能工作丰富的团体联系，以探讨逻辑的使用来理论改变世界状态的原因和描述世界的变量。拟议的研究计划是基于我以前的工作。特别是，我积极参与称为归纳逻辑编程的机器学习子区域将是有用的。我建议解释使用逻辑深度学习获得的模型。使用归纳逻辑编程将使我们能够构建与深度学习获得的模型相似的模型。这些ILP模型将从深度学习模型中“蒸馏”，并将作为一阶逻辑中的规则表达。这将使他们可以被人类解释。它还将促进将逻辑中表达的先前知识与学习的模型整合在一起。 即使是因果关系研究的部分成功也可能会产生重大影响。因果关系对于更广泛的社会接受是对使用机器学习开发用于人类决策的模型的更广泛的社会接受。例如，欧盟GDPR指令规定，任何此类模型都应解释，即该模型做出决定的人应该能够对模型的决定进行解释。理解模型最终将使我们避免根据性别，种族等做出决定的模型。我们将研究其自动化保险蛤处理系统做出的决定的解释性。解决因果关系是一个巨大的挑战。在此计划中，我建议将深度学习模型蒸馏成可理解的模型，从而使因果关系明确，并能够将多种因素分配为模型预测的给定效应的组合原因。我还将继续为机器学习而在因果关系方面继续重要工作的年轻研究人员。