WenjieWWJ / Causal_Reading_Group

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
Causality in NLP
Causal Interpretability

Survey Paper

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

Generative Causal Explanations for Graph Neural Networks(ICML2021)
Learning Individually Fair Classifier with Path-Specific Causal-Effect Constraint(2021AISTATS)
Path-specific Counterfactual Fairness (2019AAAI)
Counterfactual Fairness (2017 NIPS)
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

Deconfounded Recommendation for Alleviating Bias Amplification (2021 KDD)
Model-Agnostic Counterfactual Reasoning for Eliminating Popularity Bias in Recommender System (2021 KDD)
Causal Intervention for Leveraging Popularity Bias in Recommendation(2021 SIGIR)
Clicks can be Cheating: Counterfactual Recommendation for Mitigating Clickbait Issue (2021SIGIR)
The Simpson’s Paradox in the Offline Evaluation of Recommendation Systems(2021)
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)
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

Everything Has a Cause: Leveraging Causal Inference in Legal Text Analysis (2021NAACL)
How to make causal inferences using texts (2018arxiv)
Text and Causal Inference:A Review of Using Text to Remove Confounding from Causal Estimates (ACL2020)
Causal inference of script knowledge (2020EMNLP)
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 Interpretability

Causal Discovery

Causal Inference with Deep Causal Graphs
Causal Discovery with Reinforcement Learning(2020ICLR)
Causal Discovery from Incomplete Data: A Deep Learning Approach(2020AAAI)
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

Causal Intervention for Weakly-Supervised Semantic Segmentation (2020NeurIPS)
Long-Tailed Classification by Keeping the Good and Removing the Bad Momentum Causal Effect (2020NeurIPS)
Interventional Few-Shot Learning (2020NeurIPS)
GAN Disssertion: Visualizing and Understnding Generative Adversarial Networks (2018ICLR)
Causal Learning and Explanation of Deep Neural Networks via Autoencoded Activations (2018)

Counterfactual Interpretability

Algorithmic Recourse: from Counterfactual Explanations to Interventions (2021FAT)
CF-GNNExplainer: Counterfactual Explanations for Graph Neural Networks (2021)
Delta-CLUE Divere Sets of Explanations for Uncertainty Estimates (2021ICLR workshop)
Explaining Deep Graph Networks with Molecular Counterfactuals (2020NeurIPS)
Learning the Difference that Makes a Difference with Counterfactually-augmented Data (2020ICLR)
Counterfactual Generator: A Weakly-Supervised Method for Named Entity Recognition (2020EMNLP)
Counterfactual Visual Explanations (2019ICML)
Preserving Causal Constraints in Counterfactual Explanations for Machine Learning Classiﬁers (2019NeurIPS)
Explaining Image Classifiers by Counterfactual Generation (2019ICLR)
Interpretable Counterfactual Explanations Guided by Prototypes (2019)
Explaining Machine Learning Classifiers through Diverse Counterfactual Explanations (2019FAT)

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