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根据模型运行领域的知识来理解机器学习模型提供的决策的能力，以及推理此类决策的能力。因果模型具有自我推理的“内省”能力。学习因果模型是一项比当前机器学习方法（包括深度学习）执行的任务要困难得多的任务，深度学习确定模型输入与其决策之间的“相关”或“模式匹配”关系。我在这里提出了一个机器学习因果关系的研究计划。因果关系是机器学习领域面临的主要挑战之一。此外，拥有模型的因果表示将使机器学习朝着人类智能的能力迈进，例如通过一些例子进行学习。我建议与现有的丰富的人工智能工作联系起来，探索使用逻辑来推理世界状态变化的原因和描述世界的变量。拟议的研究计划是基于我之前的工作。特别是，我对机器学习的一个子领域（称为归纳逻辑编程）的积极参与将很有用。我建议使用逻辑来解释通过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行为类似于深度学习获得的模型的模型。这些 ILP 模型将从深度学习模型中“提炼”出来，并表示为一阶逻辑中的规则。这将使它们能够被人类解释。它还将有助于将以前以逻辑表达的知识与学习到的模型相结合。 即使因果关系研究取得部分成功也可能产生重大影响。因果关系对于更广泛的社会接受使用机器学习开发的用于人类决策的模型是必要的。例如，欧盟 GDPR 指令规定任何此类模型都应该是可解释的，即模型做出决策的人应该能够获得他们可以理解的模型决策的解释。理解模型最终将使我们能够避免基于性别、种族等做出人类决策的模型。例如，与我合作的比萨团队可以访问意大利领先保险公司之一的索赔处理数据。我们将研究他们的自动化保险蛤处理系统做出的决策的可解释性。解决因果关系是一个巨大的挑战。在这个项目中，我建议将深度学习模型提炼为可理解的模型，使因果关系变得明确，并能够将多个因素指定为模型预测的给定效果的组合原因。我还将邀请年轻的研究人员继续在机器学习的因果关系方面开展重要工作。