This is a list of top papers about causal inference and bias problem in recommendation
- Survey paper
- Robust Learning / OOD
- Causality in Computer Vision
- Causality in NLP
- Causality in Graph
- Causality in Recommender System
- Learning to Reweight Examples for Robust Deep Learning (ICML 18)
- Long-Tailed Classification by Keeping the Good and Removing the Bad Momentum Causal Effect (NIPS 20)
- CASTLE: Regularization via Auxiliary Causal Graph Discovery (NIPS 20)
- Asymmetry Learning for Counterfactually-invariant Classification in OOD Tasks (ICLR 22)
- Should Graph Convolution Trust Neighbors? A Simple Causal Inference Method (SIGIR 21)
- Discovering Invariant Rationales for Graph Neural Networks (ICLR 22)
- Causal Embeddings for Recommendation (RecSys18)
- A General Knowledge Distillation Framework for Counterfactual Recommendation via Uniform Data (SIGIR 20)
- ESAM: Discriminative domain adaptation with non-displayed items to improve long-tail performance (SIGIR 20)
- AutoDebias: Learning to Debias for Recommendation (SIGIR 21)
- Deconfounded Recommendation for Alleviating Bias Amplification (KDD 21)
- CausPref: Causal Preference Learning for Out-of-Distribution Recommendation (WWW 22)
- Causal Representation Learning for Out-of-Distribution Recommendation (WWW 22)
- Causal Inference for Recommendation (UAI 16)
- Causal inference for recommender systems (RecSys 20)
- Disentangling User Interest and Conformity for Recommendation with Causal Embedding (WWW 21)
- Causal Intervention for Leveraging Popularity Bias in Recommendation (SIGIR 21)
- Model-Agnostic Counterfactual Reasoning for Eliminating Popularity Bias in Recommender System (KDD 21)
- Popularity-Opportunity Bias in Collaborative Filtering (WSDM 21)