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https://github.com/huckiyang/awesome-deep-causal-learning
A curated list of awesome deep causal learning methods since 2018
https://github.com/huckiyang/awesome-deep-causal-learning
List: awesome-deep-causal-learning
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A curated list of awesome deep causal learning methods since 2018
- Host: GitHub
- URL: https://github.com/huckiyang/awesome-deep-causal-learning
- Owner: huckiyang
- Created: 2019-11-26T01:18:56.000Z (about 5 years ago)
- Default Branch: master
- Last Pushed: 2023-01-20T13:15:58.000Z (almost 2 years ago)
- Last Synced: 2024-05-20T09:04:36.941Z (7 months ago)
- Homepage:
- Size: 75.2 KB
- Stars: 15
- Watchers: 4
- Forks: 4
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- Awesome-Paper-List - Deep Causal Learning
README
# awesome-deep-causal-learning [](https://awesome.re)
This collection is initiated in 2018.
### A curated list of awesome deep causal learning methods - when causaliy deep meets deep neural network.
Inspired by [awesome-deep-vision](https://github.com/kjw0612/awesome-deep-vision), [awesome-adversarial-machine-learning](https://github.com/yenchenlin/awesome-adversarial-machine-learning), [awesome-deep-learning-papers](https://github.com/terryum/awesome-deep-learning-papers), [awesome-architecture-search](https://github.com/markdtw/awesome-architecture-search), [awesome-deep-neuroevolution](https://github.com/Alro10/awesome-deep-neuroevolution) (nice idea for the code index) and [awesome-self-supervised-learning](https://github.com/jason718/awesome-self-supervised-learning).
Learning to inference and disentangle is the next big challenge of Deep Learning.
Welcome to commit and pull request. I will update some guideline on causal software, which could be found out [here](https://github.com/huckiyang/awesome-deep-causal-learning/blob/master/causal_learning_software.md).
## Causal Inference
| Title | Authors | Code | Year |
| ----- | ------- | -------- | ---- |
|[Long-Tailed Classification by Keeping the Good and Removing the Bad Momentum Causal Effect](https://proceedings.neurips.cc//paper/2020/file/1091660f3dff84fd648efe31391c5524-Paper.pdf)|Tang et al.|[code](https://github.com/KaihuaTang/Long-Tailed-Recognition.pytorch)|NeurIPS 2020|
|[Causal Intervention for Weakly-Supervised Semantic Segmentation](https://proceedings.neurips.cc/paper/2020/file/07211688a0869d995947a8fb11b215d6-Paper.pdf)|Zhang et al.|[code](https://github.com/ZHANGDONG-NJUST/CONTA)|NeurIPS 2020|
|[A Meta-Transfer Objective for Learning to Disentangle Causal Mechanisms](https://arxiv.org/abs/1901.10912)|Yoshua Bengio et al.|[code](https://github.com/authors-1901-10912/A-Meta-Transfer-Objective-For-Learning-To-Disentangle-Causal-Mechanisms)|ICLR 2020|
|[Causal Induction from Visual Observations for Goal Directed Tasks](https://arxiv.org/abs/1910.01751)|Suraj Nair, et al.|-|arxiv 2019|
| [Granger-causal attentive mixtures of experts: Learning important features with neural networks](https://wvvw.aaai.org/ojs/index.php/AAAI/article/download/4412/4290)|Patrick Schwab, et al.|-|AAAI 2019|
| [Causal bandits: Learning good interventions via causal inference](http://papers.nips.cc/paper/6195-causal-bandits-learning-good-interventions-via-causal-inference) |Finnian Lattimore et al.| -|NeurIPS, 2016|
|[Learning granger causality for hawkes processes](http://proceedings.mlr.press/v48/xuc16.pdf)| Xu ,et al.|- |ICML 2016|
| [One-shot learning by inverting a compositional causal process](http://papers.nips.cc/paper/5128-one-shot-learning-by-inverting-a-compositional-causal-process)| Brenden M. Lake, et al. | - | NeurIPS 2013 |
## Causal Reinforcement Learning
| Title | Authors | Code | Year |
| ----- | ------- | -------- | ---- |
|[Training a Resilient Q-network against Observational Interference](https://arxiv.org/pdf/2102.09677.pdf)|CHH Yang et al.|[code](https://github.com/huckiyang/Obs-Causal-Q-Network)|AAAI 2022|
|[Off-policyevaluation in infinite-horizon reinforcement learning with latent confounders](https://arxiv.org/pdf/2007.13893.pdf)|Andrew Bennett et al.|-|AISTATS 2021|
## Treatment Effect Estimation
| Title | Authors | Code | Year |
| ----- | ------- | -------- | ---- |
|[Estimating identifiable causal effects through double machine learning](https://ojs.aaai.org/index.php/AAAI/article/view/17438/17245)|Y Jung et al.|-|AAAI 2021|
|[Causal effect inference with deep latent-variable models](https://proceedings.neurips.cc/paper/2017/file/94b5bde6de888ddf9cde6748ad2523d1-Paper.pdf)| Louizos, et al.|[code](https://github.com/AMLab-Amsterdam/CEVAE)|NIPS 2017|
|[Estimating individual treatment effect: generalization bounds and algorithms](http://proceedings.mlr.press/v70/shalit17a/shalit17a.pdf)| Uri Shalit, et al.|[code](https://github.com/clinicalml/cfrnet)|ICML 2017|
|[Towards a learning theory of cause-effect inference](http://proceedings.mlr.press/v37/lopez-paz15.pdf)|Lopez Paz, et al.|-|ICML 2015|
## Vision
| Title | Authors | Code | Year |
| ----- | ------- | -------- | ---- |
|[Interventional Few-Shot Learning](https://proceedings.neurips.cc/paper/2020/file/1cc8a8ea51cd0adddf5dab504a285915-Paper.pdf)|Yue et al.|[code](https://github.com/yue-zhongqi/ifsl)|NeurIPS 2020|
|[Counterfactual Vision and Language Learning](https://openaccess.thecvf.com/content_CVPR_2020/papers/Abbasnejad_Counterfactual_Vision_and_Language_Learning_CVPR_2020_paper.pdf)|Abbasnejad et al.|-|CVPR 2020|
|[Towards Causal VQA: Revealing and Reducing Spurious Correlations by Invariant and Covariant Semantic Editing](https://arxiv.org/abs/1912.07538)|Agarwal et al.|[code](https://github.com/AgarwalVedika/CausalVQA)|CVPR 2020|
|[Two Causal Principles for Improving Visual Dialog](https://arxiv.org/abs/1911.10496)| Qi et al. |[code](https://github.com/simpleshinobu/visdial-principles)|CVPR 2020|
|[Unbiased Scene Graph Generation from Biased Training](https://arxiv.org/abs/2002.11949)|Tang et al.|[code](https://github.com/KaihuaTang/Scene-Graph-Benchmark.pytorch)|CVPR 2020|
|[When Causal Intervention Meets Adversarial Examples and Image Masking for Deep Neural Networks](https://arxiv.org/abs/1902.03380)|CHH Yang, et al|[code](https://github.com/jjaacckkyy63/Causal-Intervention-AE-wAdvImg)|ICIP 2019|
| [Discovering causal signals in images](http://openaccess.thecvf.com/content_cvpr_2017/html/Lopez-Paz_Discovering_Causal_Signals_CVPR_2017_paper.html)|Lopez-Paz et al.|code withdrawn from author|CVPR 2017|
| [Causal graph-based video segmentation](https://ieeexplore.ieee.org/abstract/document/6738875)|Couprie,et al.|-|ICIP 2013|
### Contact
[C.-H. Huck Yang](https://huckiyang.github.io/), Georgia Tech and welcome to all!
2022 May 1st updated.
2021 updated.
2018 updated.