  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.
