  Bundle Recommendation (BR) aims at recommending bundled items on online
content or e-commerce platform, such as song lists on a music platform or book
lists on a reading website. Several graph based models have achieved
state-of-the-art performance on BR task. But their performance is still
sub-optimal, since the data sparsity problem tends to be more severe in real
bundle recommendation scenarios, which limits graph-based models from more
sufficient learning. In this paper, we propose a novel graph learning paradigm
called Counterfactual Learning for Bundle Recommendation (CLBR) to mitigate the
impact of data sparsity problem and improve bundle recommendation. Our paradigm
consists of two main parts: counterfactual data augmentation and counterfactual
constraint. The main idea of our paradigm lies in answering the counterfactual
questions: "What would a user interact with if his/her interaction history
changes?" "What would a user interact with if the bundle-item affiliation
relations change?" In counterfactual data augmentation, we design a heuristic
sampler to generate counterfactual graph views for graph-based models, which
has better noise controlling than the stochastic sampler. We further propose
counterfactual loss to constrain model learning for mitigating the effects of
residual noise in augmented data and achieving more sufficient model
optimization. Further theoretical analysis demonstrates the rationality of our
design. Extensive experiments of BR models applied with our paradigm on two
real-world datasets are conducted to verify the effectiveness of the paradigm.
