This is a list of papers about causality.
- Survey paper
- Dataset
- Foundamental Causality
- Causality in Machine Learning
- Causal Recommendation
- Causal Computer Vision
- Causality in NLP
- Causal Interpretability
- Causal Interpretability for Machine Learning -- Problems, Methods and Evaluation (2020KDD)
- A Survey of Learning Causality with Data: Problems and Methods (2020)
- A Survey on Causal Inference (2020)
- ACIC 2018 Data Challenge (2018ACIC)
- Meta-learners for Estimating Heterogeneous Treatment Effects using Machine Learning (2019PNAS)
- Unit Selection Based on Counterfactual Logic (2019IJCAI)
- Counterfactual regression with importance sampling weights (2019IJCAI)
- Orthogonal Random Forest for Causal Inference (2019ICML)
- Estimation and Inference of Heterogeneous Treatment Effects using Random Forests (2018JASA)
- Estimating individual treatment effect: generalization bounds and algorithms (2017JMLR)
- Towards a learning theory of cause-effect inference (2015ICML)
- A Causal View on Robustness of Neural Networks (2020NeurIPS)
- An investigation of why overparameterization exacerbates spurious correlations (2020)
- Matching in Selective and Balanced Representation Space for Treatment Effects Estimation (2020CIKM)
- Improving the accuracy of medical diagnosis with causal machine learning (2020Nature Communication)
- Causal Meta-Mediation Analysis: Inferring Dose-Response Function From Summary Statistics of Many Randomized Experiments (2020KDD)
- Adapting Text Embeddings for Causal Inference (2020UAI)
- Double/Debiased/Neyman Machine Learning of Treatment Effects (2017American Economic Review)
- Bayesian Regression Tree Models for Causal Inference: Regularization, Confounding, and Heterogeneous Effects(2020)
- Deep IV: A Flexible Approach for Counterfactual Prediction(2017)
- Estimating individual treatment effect: generalization bounds and algorithms(2017)
- Causal Decision Trees(2020)
- Estimating Individual Treatment Effect in Observational Data Using Random Forest Methods(2018)
- PRINCE: Provider-side Interpretability with Counterfactual Explanations in Recommender Systems (2020WSDM)
- Counterfactual Prediction for Bundle Treatment (2020NeurIPS)
- Adversarial Counterfactual Learning and Evaluation for Recommender System (2020NeurIPS)
- Information Theoretic Counterfactual Learning from Missing-Not-At-Random Feedback (2020NeurIPS)
- Model-Agnostic Counterfactual Reasoning for Eliminating Popularity Bias in Recommender System (2020)
- "Click" Is Not Equal to "Like": Counterfactual Recommendation for Mitigating Clickbait Issue (2020)
- Learning Stable Graphs from Multiple Environments with Selection Bias (2020KDD)
- Causal Inference for Recommender Systems (2020 RecSys)
- Debiasing Item-to-Item Recommendations With Small Annotated Datasets (2020 RecSys)
- Deconfounding User Satisfaction Estimation from Response Rate Bias (2020 RecSys)
- Unbiased Learning for the Causal Effect of Recommendation (2020 RecSys)
- Unbiased Recommender Learning from Missing-Not-At-Random Implicit Feedback (2020WSDM)
- A General Framework for Counterfactual Learning-to-Rank (2019SIGIR)
- Doubly Robust Joint Learning for Recommendation on Data Missing Not at Random (2019ICML)
- Causal Embeddings for Recommendation: An Extended Abstract (2019IJCAI)
- Unbiased Learning to Rank with Unbiased Propensity Estimation (2018SIGIR)
- Recommendations as Treatments: Debiasing Learning and Evaluation (2016ICML)
- Estimating the Causal Impact of Recommendation Systems from Observational Data (2015ACMEC)
- Evaluating Online Ad Campaigns in a Pipeline: Causal Models At Scale (2010KDD)
- Causal Attention for Vision-Language Tasks (2021CVPR)
- Deconfounded Image Captioning: A Causal Retrospect
- Counterfactual VQA: A Cause-Effect Look at Language Bias (2021CVPR)
- Visual Commonsense R-CNN (2020CVPR)
- More Grounded Image Captioning by Distilling Image-Text Matching Model (2020CVPR)
- Visual Commonsense Representation Learning via Causal Inference (2020CVPR)
- Counterfactual Samples Synthesizing for Robust Visual Question Answering (2020CVPR)
- Unbiased Scene Graph Generation From Biased Training (2020CVPR)
- Two Causal Principles for Improving Visual Dialog (2020CVPR)
- Counterfactual Vision-and-Language Navigation via Adversarial Path Sampling (2020ECCV)
- De-Biased Court’s View Generation with Causality (2020EMNLP)
- Counterfactual Generator: A Weakly-Supervised Method for Named Entity Recognition (2020EMNLP)
- Counterfactual Off-Policy Training for Neural Dialogue Generation (2020EMNLP)
- Identifying Spurious Correlations for Robust Text Classification (2020EMNLP)
- Feature Selection as Causal Inference: Experiments with Text Classification (2017CoNLL)
- Causal Inference with Deep Causal Graphs
- Causal Discovery with Reinforcement Learning(ICLR)
- Causal Discovery from Incomplete Data: A Deep Learning Approach(AAAI)
- A Graph Autoencoder Approach to Causal Structure Learning (2019-NeurIPS)
- CXPlain: Causal Explanations for Model Interpretation under Uncertainty (2019-NeurIPS)
- Neural Network Attributions: A Causal Perspective (2019-ICML)
- Building Causal Graphs from Medical Literature and Electronic Medical Records(2019-AAAI)
- Explaining Deep Learning Models Using Causal Inference (2018)
- Neural Relational Inference for Interacting Systems (2018-ICML)
- Learning Independent Causal Mechanisms (2018-ICML)
- DAGs with NO TEARS: Continuous Optimization for Structure Learning (2018-NeurIPS)
- A Causal Framework for Explaining the Predictions of Black-box Sequence-to-sequence Models (2017-EMNLP)
- Causal Intervention for Weakly-Supervised Semantic Segmentation (NeurIPS)
- Long-Tailed Classification by Keeping the Good and Removing the Bad Momentum Causal Effect (NeurIPS)
- Interventional Few-Shot Learning (NeurIPS)