Causal reasoning and logical reasoning are two important types of reasoning abilities for human intelligence. However, their relationship has not been extensively explored under machine intelligence context. In this paper, we explore how the two reasoning abilities can be jointly modeled to enhance both accuracy and explainability of machine learning models. More specifically, by integrating two important types of reasoning ability -- counterfactual reasoning and (neural) logical reasoning -- we propose Counterfactual Collaborative Reasoning (CCR), which conducts counterfactual logic reasoning to improve the performance. In particular, we use recommender system as an example to show how CCR alleviate data scarcity, improve accuracy and enhance transparency. Technically, we leverage counterfactual reasoning to generate "difficult" counterfactual training examples for data augmentation, which -- together with the original training examples -- can enhance the model performance. Since the augmented data is model irrelevant, they can be used to enhance any model, enabling the wide applicability of the technique. Besides, most of the existing data augmentation methods focus on "implicit data augmentation" over users' implicit feedback, while our framework conducts "explicit data augmentation" over users explicit feedback based on counterfactual logic reasoning. Experiments on three real-world datasets show that CCR achieves better performance than non-augmented models and implicitly augmented models, and also improves model transparency by generating counterfactual explanations.

翻译：因果推理与逻辑推理是人类智能的两类重要推理能力，然而在机器智能背景下这两者之间的关系尚未得到充分探索。本文旨在研究如何通过联合建模这两种推理能力增强机器学习模型的准确性与可解释性。具体而言，通过整合反事实推理与（神经）逻辑推理两种关键能力，我们提出反事实协同推理（CCR）框架，该框架通过执行反事实逻辑推理来提升模型性能。我们以推荐系统为例，展示CCR如何缓解数据稀疏性、提升准确率并增强透明度。在技术层面，我们利用反事实推理生成"困难"的反事实训练样本进行数据增强，这些样本与原始训练样本共同作用可提升模型性能。由于增强数据与原始模型无关，因此可适用于任何模型，使该技术具有广泛适用性。此外，现有数据增强方法大多针对用户隐式反馈进行"隐式数据增强"，而我们的框架基于反事实逻辑推理对用户显式反馈执行"显式数据增强"。在三个真实数据集上的实验表明，CCR在性能上优于未增强模型及隐式增强模型，并通过生成反事实解释提升了模型透明度。