Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

https://github.com/tangxiangru/awesome-causal-inference

Reading list for research topics in causal inference.
https://github.com/tangxiangru/awesome-causal-inference

List: awesome-causal-inference

Last synced: 4 days ago
JSON representation

Reading list for research topics in causal inference.

Lists

README 

        
# Reading List for Topics in Causal Inference.



If there are any areas, papers, and datasets I missed, please let me know!



## CVPR 2020

- Unbiased Scene Graph Generation from Biased Training [[pdf]](https://arxiv.org/pdf/2002.11949.pdf)

- Visual Commonsense R-CNN [[pdf]](https://arxiv.org/pdf/2002.12204.pdf)



## AAAI 2020

- [Causally Denoise Word Embeddings Using Half-Sibling Regression](http://discourse.mlbrainstorm.club/t/topic/458) [[pdf]](https://arxiv.org/pdf/1911.10524.pdf)

- A Causal Inference Method for Reducing Gender Bias in Word Embedding Relations [[pdf]](https://arxiv.org/pdf/1911.10787.pdf)

- [\*] [A Simultaneous Discover-Identify Approach to Causal Inference in Linear Models](http://discourse.mlbrainstorm.club/t/topic/462) [[pdf]](https://ftp.cs.ucla.edu/pub/stat_ser/r491-L.pdf)

- [\*] Explainable Reinforcement Learning Through a Causal Lens [[pdf]](https://arxiv.org/pdf/1905.10958.pdf)

- Integrating overlapping datasets using bivariate causal discovery [[pdf]](https://arxiv.org/pdf/1910.11356.pdf)

- Theory-based Causal Transfer: Integrating Instance-level Induction and Abstract-level Structure Learning [[pdf]](https://arxiv.org/pdf/1911.11185.pdf)

- [CoCoX: Generating Conceptual and Counterfactual Explanations via Fault-Lines](http://discourse.mlbrainstorm.club/t/topic/460/2) [[pdf]](https://pdfs.semanticscholar.org/d03e/66a84b92f520235079083d3c0947b2c910e0.pdf)

- Causal Transfer for Imitation Learning and Decision Making under Sensor-shift

- Recovering Causal Structures from Low-Order Conditional Independencies

- A Bayesian Approach for Estimating Causal Effects From Observational Data

- Estimating Causal Effects using Weighting-based Estimators

- A Calculus for Stochastic Interventions: Causal Effect Identification and Surrogate Experiment

- Multi-Source Causal Feature Selection



## ICLR 2020

- [\*] Counterfactuals uncover the modular structure of deep generative models [[pdf]](https://openreview.net/pdf?id=SJxDDpEKvH)

- [\*] [Learning Disentangled Representations for CounterFactual Regression](http://discourse.mlbrainstorm.club/t/topic/417) [[pdf]](https://openreview.net/pdf?id=HkxBJT4YvB)

- [\*] [Learning The Difference That Makes A Difference With Counterfactually-Augmented Data](http://discourse.mlbrainstorm.club/t/topic/419/3) [[pdf]](https://openreview.net/pdf?id=Sklgs0NFvr)

-  [\*] [Explanation by Progressive Exaggeration](http://discourse.mlbrainstorm.club/t/topic/420) [[pdf]](https://openreview.net/pdf?id=H1xFWgrFPS)

- [\*] [Estimating counterfactual treatment outcomes over time through adversarially balanced representations](http://discourse.mlbrainstorm.club/t/topic/464) [[pdf]](https://openreview.net/pdf?id=BJg866NFvB)

- [A Meta-Transfer Objective for Learning to Disentangle Causal Mechanisms](http://discourse.mlbrainstorm.club/t/topic/466) [[pdf]](https://openreview.net/pdf?id=ryxWIgBFPS)

- [\*] [Causal Discovery with Reinforcement Learning](http://discourse.mlbrainstorm.club/t/topic/439) [[pdf]](https://openreview.net/pdf?id=S1g2skStPB)

- [ ] CoPhy: Counterfactual Learning of Physical Dynamics [[pdf](https://openreview.net/pdf?id=SkeyppEFvS)]



## ICLR 2020 (rejected)

- [\*] [A Causal View on Robustness of Neural Networks](http://discourse.mlbrainstorm.club/t/topic/438) [[pdf]](https://openreview.net/forum?id=Hkxvl0EtDH)

- MissDeepCausal: causal inference from incomplete data using deep latent variable models [[pdf]](https://openreview.net/pdf?id=SylpBgrKPH)



## FAT* 2020

- Explaining Machine Learning Classifiers through Diverse Counterfactual Explanations [[pdf]](https://arxiv.org/pdf/1905.07697.pdf)



## AAAI 2019

- [\*] [Desiderata for Interpretability: Explaining Decision Tree Predictions with Counterfactuals](http://discourse.mlbrainstorm.club/t/topic/432) [[pdf]](https://www.aaai.org/ojs/index.php/AAAI/article/download/5154/5027https://www.aaai.org/ojs/index.php/AAAI/article/download/5154/5027)



## ACL 2019

- [\*] [Counterfactual Data Augmentation for Mitigating Gender Stereotypes in Languages with Rich Morphology](http://discourse.mlbrainstorm.club/t/topic/426/2) [[pdf]](https://www.aclweb.org/anthology/P19-1161.pdf)



## EMNLP 2019

- [\*] [Counterfactual Story Reasoning and Generation](http://discourse.mlbrainstorm.club/t/topic/428) [[pdf]](https://arxiv.org/pdf/1909.04076.pdf)



## WWW 2019

- [\*] [Fairness in Algorithmic Decision Making: An Excursion Through the Lens of Causality](http://discourse.mlbrainstorm.club/t/topic/445) [[pdf]](https://arxiv.org/pdf/1903.11719.pdf)



## SIGIR 2019

- [\*] [A General Framework for Counterfactual Learning-to-Rank](http://discourse.mlbrainstorm.club/t/topic/430) [[pdf]](http://www.cs.cornell.edu/people/tj/publications/agarwal_etal_19b.pdf)



## CIKM 2019

- [\*] Scalable Causal Graph Learning through a Deep Neural Network [[pdf]](https://www.osti.gov/servlets/purl/1566865)



## IJCAI 2019

- [\*] [The Dangers of Post-hoc Interpretability: Unjustified Counterfactual Explanations](http://discourse.mlbrainstorm.club/t/topic/431) [[pdf]](https://www.ijcai.org/proceedings/2019/0388.pdf)

- [\*] [Counterfactuals in Explainable Artificial Intelligence (XAI): Evidence from Human Reasoning](http://discourse.mlbrainstorm.club/t/topic/427) [[pdf]](https://www.ijcai.org/proceedings/2019/0876.pdf)

- [\*] [Causal Embeddings for Recommendation: An Extended Abstract](http://discourse.mlbrainstorm.club/t/topic/421) [[pdf]](https://www.ijcai.org/proceedings/2019/0870.pdf)



## CVPR 2019

- [\*] [Multimodal Explanations by Predicting Counterfactuality in Videos](http://discourse.mlbrainstorm.club/t/topic/422) [[pdf]](https://arxiv.org/pdf/1812.01263.pdf)



## CVPR Workshop 2019



- [\*] [Question-Conditioned Counterfactual Image Generation for VQA](http://discourse.mlbrainstorm.club/t/topic/423) [[pdf]](https://arxiv.org/pdf/1911.06352.pdf)



## ICLR 2019

- [Explaining Image Classifiers by Counterfactual Generation](http://discourse.mlbrainstorm.club/t/topic/437) [[pdf]](https://openreview.net/pdf?id=B1MXz20cYQ)

- [\*] Woulda, Coulda, Shoulda: Counterfactually-Guided Policy Search [[pdf]](https://arxiv.org/pdf/1811.06272v1.pdf)



## NIPS 2019

- [\*] [CXPlain: Causal Explanations for Model Interpretation under Uncertainty](http://discourse.mlbrainstorm.club/t/topic/429)  [[pdf]](https://papers.nips.cc/paper/9211-cxplain-causal-explanations-for-model-interpretation-under-uncertainty.pdf)

- [A Game Theoretic Approach to Class-wise Selective Rationalization](http://discourse.mlbrainstorm.club/t/topic/440) [[pdf]](https://papers.nips.cc/paper/9196-a-game-theoretic-approach-to-class-wise-selective-rationalization.pdf)

- Causal Confusion in Imitation Learning



## ICML 2019

- Bayesian Counterfactual Risk Minimization [[pdf]](https://arxiv.org/pdf/1806.11500v1.pdf)

- [\*] [Causal Identification under Markov Equivalence: Completeness Results](http://discourse.mlbrainstorm.club/t/topic/415) [[pdf]](https://arxiv.org/pdf/1812.06209.pdf)

- [\*] Counterfactual Off-Policy Evaluation with Gumbel-Max Structural Causal Models [[pdf]](https://arxiv.org/pdf/1905.05824.pdf)

- [Counterfactual Visual Explanations](http://discourse.mlbrainstorm.club/t/topic/346) [[pdf]](https://arxiv.org/pdf/1904.07451.pdf)

- [\*] [Deep Counterfactual Regret Minimization](http://discourse.mlbrainstorm.club/t/topic/447) [[pdf]](https://arxiv.org/pdf/1811.00164.pdf)

- [\*] [Learning to Generalize from Sparse and Underspecified Rewards](http://discourse.mlbrainstorm.club/t/topic/399) [[pdf]](https://arxiv.org/pdf/1902.07198.pdf)

- [Repairing without Retraining: Avoiding Disparate Impact with Counterfactual Distributions](http://discourse.mlbrainstorm.club/t/topic/436) [[pdf]](https://arxiv.org/pdf/1901.10501.pdf)

- [Validating Causal Inference Models via Influence Functions](http://discourse.mlbrainstorm.club/t/topic/347) [[pdf]](http://proceedings.mlr.press/v97/alaa19a/alaa19a.pdf)

- Robustly Disentangled Causal Mechanisms: Validating Deep Representations for Interventional Robustness [[pdf]](https://arxiv.org/pdf/1811.00007.pdf)



## ECCV 2018

- [Open Set Learning with Counterfactual Images](http://discourse.mlbrainstorm.club/t/topic/452) [[pdf]](http://web.engr.oregonstate.edu/~lif/3090.pdf)



## ICLR 2018

- [\*] [Calsalgan：Learning Causal Implicit Generative Models with Adversarial Training](http://discourse.mlbrainstorm.club/t/topic/425) [[pdf]](https://arxiv.org/pdf/1709.02023.pdf)



## EMNLP 2017

- [\*] [A causal framework for explaining the predictions of black box sequence to sequence models](http://discourse.mlbrainstorm.club/t/topic/424) [[pdf]](https://arxiv.org/pdf/1707.01943.pdf)



## Others

- [\*] [Generating Counterfactual Explanations with Natural Language](http://discourse.mlbrainstorm.club/t/topic/435) [[pdf]](https://arxiv.org/pdf/1806.09809.pdf) (ICML 2018 Workshop)

- [\*] [Counterfactual Vision-and-Language Navigation via Adversarial Path Sampling](http://discourse.mlbrainstorm.club/t/topic/418) [[pdf]](https://arxiv.org/pdf/1911.07308.pdf) (arxiv)

- Counterfactual Reasoning and Learning Systems: The Example of Computational Advertising [[pdf]()] (JMLR 2013)