awesome-causality-algorithms
github.com/rguo12/awesome-causality-algorithms ↗An index of algorithms for learning causality with data
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ToolboxesCausal Machine LearningCausal Discovery
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Causal Discovery
- Amortized Inference for Causal Structure Learningfor i.i.d. Data
Neurips 2022
- DAG-GNN: DAG Structure Learning with Graph Neural Networksfor i.i.d. Data
ICML 2019
- DAGs with NO TEARS: Continuous optimization for structure learningfor i.i.d. Data
NeurIPS 2018
- Learning instrumental variables with structural and non-gaussianity assumptionsfor i.i.d. Data
JMLR
- Learning Sparse Nonparametric DAGsfor i.i.d. Data
AISTATS 2020
Toolboxes
- Bench PressCausal Discovery
Benchpress: a scalable and versatile workflow for benchmarking structure learning algorithms for graphical models
- causal-learnCausal Discovery
Causal-learn: Causal Discovery in Python
- Chaos GeniusRootcause Analysis
NA
- TETRAD R/JavaCausal Discovery
TETRAD-A Toolbox FOR CAUSAL DISCOVERY
- YLearnComprehensive
Python
Causal Machine Learning
- Chiappa, Silvia. "Path-specific counterfactual fairness." In Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 7801-7808. 2019.Counterfactual Fairness
code
- Garg, Sahaj, Vincent Perot, Nicole Limtiaco, Ankur Taly, Ed H. Chi, and Alex Beutel. "Counterfactual fairness in text classification through robustness." In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society, pp. 219-226. 2019.Counterfactual Fairness
code
- Kusner, Matt J., Joshua Loftus, Chris Russell, and Ricardo Silva. "Counterfactual fairness." In Advances in Neural Information Processing Systems, pp. 4066-4076. 2017.Counterfactual Fairness
Python
- Mothilal, Ramaravind Kommiya, Amit Sharma, and Chenhao Tan. "Explaining machine learning classifiers through diverse counterfactual explanations." arXiv preprint arXiv:1905.07697 (2019).Counterfactual Explanations
Python
- Russell, Chris. "Efficient search for diverse coherent explanations." In Proceedings of the Conference on Fairness, Accountability, and Transparency, pp. 20-28. 2019.Counterfactual Explanations
Python
- Wachter, Sandra, Brent Mittelstadt, and Chris Russell. "Counterfactual explanations without opening the black box: Automated decisions and the GDPR." Harv. JL & Tech. 31 (2017): 841.Counterfactual Explanations
Showing a sample of 17 resources. View the full list on GitHub →