Causal_Reading_Group
This is a list of papers about causality.
Table of Contents
- Survey paper
- Foundamental Causality
- Causality in Machine Learning
- Causality in Recommendation
- Causality in Computer Vision
Survey Paper
Foundamental Causality
- Meta-learners for Estimating Heterogeneous Treatment Effects using Machine Learning (2019PNAS)
- Unit Selection Based on Counterfactual Logic (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)
Causality in Machine Learning
- Improving the accuracy of medical diagnosis with causal machine learning (2020Nature Communication)
- Double/Debiased/Neyman Machine Learning of Treatment Effects (2017American Economic Review)
- Feature Selection as Causal Inference: Experiments with Text Classification (2017CoNLL)
Causality in Recommendation
- Learning Stable Graphs from Multiple Environments with Selection Bias (2020KDD)
- 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)
Causality in Computer Vision
- Deconfounded Image Captioning: A Causal Retrospect
- Counterfactual VQA: A Cause-Effect Look at Language Bias
- 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)