README.md

Causal_Reading_Group

This is a list of papers about causality.

Table of Contents

• Survey paper
• Dataset
• Foundamental Causality
• Causality in Machine Learning
• Causal Recommendation
• Causal Computer Vision
• Causal Interpretability

Survey Paper

• 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)

Dataset

• ACIC 2018 Data Challenge (2018ACIC)

Foundamental Causality

• 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)

Causality in Machine Learning

• 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)

Causal Recommendation

• 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 Computer Vision

• 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)

Causality in NLP

• 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 Interpretability

Causal Representation Learning

• Neural Network Attributions: A Causal Perspective (2019ICML)
• Explaining Deep Learning Models Using Causal Inference (2018)
• A Causal Framework for Explaining the Predictions of Black-box Sequence-to-sequence Models (2017EMNLP)

Causal Intervention

• GAN Disssertion: Visualizing and Understnding Generative Adversarial Networks (2018ICLR)

• Causal Learning and Explanation of Deep Neural Networks via Autoencoded Activations (2018)

• Causal Intervention for Weakly-Supervised Semantic Segmentation (NeurIPS 2020)

• Long-Tailed Classification by Keeping the Good and Removing the Bad Momentum Causal Effect (NeurIPS 2020)

• Interventional Few-Shot Learning (NeurIPS 2020)

Counterfactual Interpretability

• Counterfactual Visual Explanations (2019ICML)
• Interpretable Counterfactual Explanations Guided by Prototypes (2019)
• Explaining Machine Learning Classifiers through Diverse Counterfactual Explanations (2019FAT)