awesome-causality-algorithms

An index of algorithms in

machine learning for causal inference: solves causal inference problems
causal machine learning: solves ML problems

Reproducibility is important! We will remove those methods without open-source code unless it is a survey/review paper.

Please cite our survey paper if this index is helpful.

@article{guo2020survey,
  title={A survey of learning causality with data: Problems and methods},
  author={Guo, Ruocheng and Cheng, Lu and Li, Jundong and Hahn, P Richard and Liu, Huan},
  journal={ACM Computing Surveys (CSUR)},
  volume={53},
  number={4},
  pages={1--37},
  year={2020},
  publisher={ACM New York, NY, USA}
}

Toolboxes
Causal Effect Estimation
Causal Machine Learning

Toolboxes

Comprehensive

Name	Paper	Code
Trustworthy AI	This repository aims to include trustworthy AI related projects from Huawei Noah's Ark Lab. Current projects include:Causal Structure Learning, Causal Disentangled Representation Learning, gCastle (or pyCastle, pCastle).	Python
YLearn	YLearn, a pun of “learn why”, is a python package for causal learning which supports various aspects of causal inference ranging from causal discovery，causal effect identification, causal effect estimation, counterfactual inference，policy learning，etc.	Python

Treatment Effect Estimation / Uplift Modeling

Name	Paper	Code
DoWhy	Amit Sharma and Emre Kiciman. "Tutorial on Causal Inference and Counterfactual Reasoning." In ACM SIGKDD 2018	Python
EconML	"Causal Inference and Machine Learning in Practice with EconML and CausalML" In EconML/CausalML KDD 2021 Tutorial	Python
Uber CausalML	Chen, Huigang, et al. "Causalml: Python package for causal machine learning." arXiv preprint arXiv:2002.11631 (2020).	Python
JustCause	For evaluation of heterogeneous treatment effect estimators on common reference as well as synthetic data. Underlying thesis	Python
WhyNot	An experimental sandbox for causal inference and decision making in dynamics. Documentation	Python
scikit-uplift	Uplift modeling in scikit-learn style in python. Documentation and User guide for uplift modeling	Python

Causal Discovery

Name	Paper	Code
Bench Press	Reproducible and scalable execution and benchmarks of 41 structure learning algorithms supporting multiple language	Code
causal-learn	Causal Discovery for Python. A translation and extension of TETRAD.	Python
TETRAD toolbox	Ramsey, Joseph D., Kun Zhang, Madelyn Glymour, Ruben Sanchez Romero, Biwei Huang, Imme Ebert-Uphoff, Savini Samarasinghe, Elizabeth A. Barnes, and Clark Glymour. "TETRAD-AToolbox FOR CAUSAL DISCOVERY."	R
Causaldag	Python package for the creation, manipulation, and learning of Causal DAGs	code
CausalNex	A toolkit for causal reasoning with Bayesian Networks.	Python
Tetrad	Tetrad is a modular, object-oriented program for causal inference.	Java
CausalDiscoveryToolbox	Kalainathan, Diviyan, and Olivier Goudet. "Causal Discovery Toolbox: Uncover causal relationships in Python." arXiv preprint arXiv:1903.02278 (2019).	Python

Other Applications

Name	Paper	Code
Chaos Genius	ML powered analytics engine for outlier/anomaly detection and root cause analysis.	Python

Causal Effect Estimation

Name	Paper	Code
Survey Paper	Yao, Liuyi, Zhixuan Chu, Sheng Li, Yaliang Li, Jing Gao, and Aidong Zhang. "A survey on causal inference." ACM Transactions on Knowledge Discovery from Data (TKDD) 15, no. 5 (2021): 1-46.	NA

With i.i.d Data

Individual Treatment Effect (ITE) / Conditional Average Treatment Effect (CATE)

Deep Learning based methods

Name	Paper	Code
TARNet, Counterfactual Regression	Shalit, Uri, Fredrik D. Johansson, and David Sontag. "Estimating individual treatment effect: generalization bounds and algorithms." In ICML 2017	Python
BNN, BLR	Johansson, Fredrik, Uri Shalit, and David Sontag. "Learning representations for counterfactual inference." In International Conference on Machine Learning, pp. 3020-3029. 2016.	Python
Causal Effect VAE	Louizos, Christos, Uri Shalit, Joris M. Mooij, David Sontag, Richard Zemel, and Max Welling. "Causal effect inference with deep latent-variable models." In Advances in Neural Information Processing Systems, pp. 6446-6456. 2017.	Python
Dragonnet	Shi, Claudia, David Blei, and Victor Veitch. "Adapting neural networks for the estimation of treatment effects." Advances in neural information processing systems 32 (2019).	Python
SITE	Yao, Liuyi, Sheng Li, Yaliang Li, Mengdi Huai, Jing Gao, and Aidong Zhang. "Representation Learning for Treatment Effect Estimation from Observational Data." In Advances in Neural Information Processing Systems, pp. 2638-2648. 2018.	Python
GANITE	Yoon, Jinsung, James Jordon, and Mihaela van der Schaar. "GANITE: Estimation of Individualized Treatment Effects using Generative Adversarial Nets." (2018).	Python
Perfect Match	Schwab, Patrick, Lorenz Linhardt, and Walter Karlen. "Perfect match: A simple method for learning representations for counterfactual inference with neural networks." arXiv preprint arXiv:1810.00656 (2018)	Python
Intact-VAE	Wu, Pengzhou, and Kenji Fukumizu. "Intact-VAE: Estimating treatment effects under unobserved confounding." ICLR 2022.	code in Openreview
CausalEGM	Qiao Liu, Zhongren Chen, and Wing Hung Wong. "CausalEGM: a general causal inference framework by encoding generative modeling.". arXiv preprint arXiv:2212.05925 (2022)	Python

Classic Methods

Name	Paper	Code
Propensity Score Matching	Rosenbaum, Paul R., and Donald B. Rubin. "The central role of the propensity score in observational studies for causal effects." Biometrika 70, no. 1 (1983): 41-55.	Python

Tree based Methods

Name	Paper	Code
Causal Forest	Wager, Stefan, and Susan Athey. "Estimation and inference of heterogeneous treatment effects using random forests." JASA (2017).	code R, code Python
Causal MARS, Causal Boosting, Pollinated Transformed Outcome Forests	S. Powers et al., “Some methods for heterogeneous treatment effect estimation in high-dimensions,” 2017.	code R, code R
Bayesian Additive Regression Trees (BART)	Hill, Jennifer L. "Bayesian nonparametric modeling for causal inference." Journal of Computational and Graphical Statistics 20, no. 1 (2011): 217-240.	Python

Meta Learner

Name	Paper	Code
X-learner	Künzel, Sören R., Jasjeet S. Sekhon, Peter J. Bickel, and Bin Yu. "Metalearners for estimating heterogeneous treatment effects using machine learning." Proceedings of the National Academy of Sciences 116, no. 10 (2019): 4156-4165.	code R, code R

Average Treatment Effect (including ATT and ATC)

Name	Paper	Code
Inverse Probability Reweighting	Rosenbaum, Paul R., and Donald B. Rubin. "The central role of the propensity score in observational studies for causal effects." Biometrika 70, no. 1 (1983): 41-55.	R
Doubly Robust Estimation	Bang, Heejung, and James M. Robins. "Doubly robust estimation in missing data and causal inference models." Biometrics 61, no. 4 (2005): 962-973.	R
Doubly Robust Estimation for High Dimensional Data	Antonelli, Joseph, Matthew Cefalu, Nathan Palmer, and Denis Agniel. "Doubly robust matching estimators for high dimensional confounding adjustment." Biometrics (2016).	R
TMLE	Gruber, Susan, and Mark J. van der Laan. "tmle: An R package for targeted maximum likelihood estimation." (2011).	R
Entropy Balancing	Hainmueller, Jens. "Entropy balancing for causal effects: A multivariate reweighting method to produce balanced samples in observational studies." Political Analysis 20, no. 1 (2012): 25-46.	R
CBPS(Covariate Balancing Propensity Score)	Imai, Kosuke, and Marc Ratkovic. "Covariate balancing propensity score." Journal of the Royal Statistical Society: Series B (Statistical Methodology) 76, no. 1 (2014): 243-263.	R
Approximate Residual Balancing	Athey, Susan, Guido W. Imbens, and Stefan Wager. "Approximate residual balancing: debiased inference of average treatment effects in high dimensions." Journal of the Royal Statistical Society: Series B (Statistical Methodology) 80, no. 4 (2018): 597-623.	R
Causal Bootstrapping	Little, Max A., and Reham Badawy. "Causal bootstrapping." arXiv preprint arXiv:1910.09648 (2019).	Matlab

Instrumental Variable (IV)

Name	Paper	Code
DeepIV	Hartford, Jason, Greg Lewis, Kevin Leyton-Brown, and Matt Taddy. "Deep iv: A flexible approach for counterfactual prediction." In International Conference on Machine Learning, pp. 1414-1423. 2017.	Python
PDSLasso	Achim Ahrens & Christian B. Hansen & Mark E Schaffer, 2018. "PDSLASSO: Stata module for post-selection and post-regularization OLS or IV estimation and inference," Statistical Software Components S458459, Boston College Department of Economics, revised 24 Jan 2019.	STATA

Does-Response Curve (Continuous Treatment)

Name	Paper	Code
Causal Dose-Response Curves / Causal Curves	Kobrosly, R. W., (2020). causal-curve: A Python Causal Inference Package to Estimate Causal Dose-Response Curves. Journal of Open Source Software, 5(52), 2523, https://doi.org/10.21105/joss.02523	Python
Dose response networks (DRNets)	Schwab, Patrick, Lorenz Linhardt, Stefan Bauer, Joachim M. Buhmann, and Walter Karlen. "Learning Counterfactual Representations for Estimating Individual Dose-Response Curves." arXiv preprint arXiv:1902.00981 (2019).	Python

Vectorized Treatments

Name	Paper	Code
Causal Effect Inference for Structured Treatments	Jean Kaddour, Qi Liu, Yuchen Zhu, Matt J. Kusner, Ricardo Silva. "Causal Effect Inference for Structured Treatments", In NeurIPS 2021.	Python

Multiple Causes

Name	Paper	Code
Deconfounder	Wang, Yixin, and David M. Blei. "The blessings of multiple causes." arXiv preprint arXiv:1805.06826 (2018).	Python

Multiple Outcomes

Name	Paper	Code
Multiple Responses in Uplift Models	Weiss, Sam. Estimating and Visualizing Business Tradeoffs in Uplift Models	Python

Non-i.i.d Data

Panel Data / Time Series

Name	Paper	Code
Synthetic Control Method	Abadie, Alberto. "Using synthetic controls: Feasibility, data requirements, and methodological aspects." Journal of Economic Literature 59.2 (2021): 391-425.	R
Synthetic Difference in Differences	Arkhangelsky, Dmitry, et al. Synthetic difference in differences. No. w25532. National Bureau of Economic Research, 2019.	R Python
Causal Impact	Brodersen, K. H., Gallusser, F., Koehler, J., Remy, N., & Scott, S. L. (2015). Inferring causal impact using Bayesian structural time-series models. The Annals of Applied Statistics, 9(1), 247–274. doi: 10.1214/14-AOAS788	R Python
Time Series Deconfounder	Bica, Ioana, Ahmed M. Alaa, and Mihaela van der Schaar. "Time Series Deconfounder: Estimating Treatment Effects over Time in the Presence of Hidden Confounders." In ICML 2020.	code
Recurrent Marginal Structural Networks	Lim, Bryan. "Forecasting Treatment Responses Over Time Using Recurrent Marginal Structural Networks." In Advances in Neural Information Processing Systems, pp. 7494-7504. 2018.	Python
Longitudinal Targeted Maximum Likelihood Estimation	Petersen, Maya, Joshua Schwab, Susan Gruber, Nello Blaser, Michael Schomaker, and Mark van der Laan. "Targeted maximum likelihood estimation for dynamic and static longitudinal marginal structural working models." Journal of causal inference 2, no. 2 (2014): 147-185.	R
Causal Transformer	Melnychuk, Valentyn, Dennis Frauen, and Stefan Feuerriegel. "Causal Transformer for Estimating Counterfactual Outcomes." arXiv preprint arXiv:2204.07258 (2022).	Python

Network Data (with or without Interference)

Name	Paper	Code
Network Deconfounder	Guo, Ruocheng, Jundong Li, and Huan Liu. "Learning Individual Causal Effects from Networked Observational Data." WSDM 2020.	Python
Causal Inference with Network Embeddings	Veitch, Victor, Yixin Wang, and David M. Blei. "Using embeddings to correct for unobserved confounding." arXiv preprint arXiv:1902.04114 (2019).	Python
Linked Causal Variational Autoencoder (LCVA)	Rakesh, Vineeth, Ruocheng Guo, Raha Moraffah, Nitin Agarwal, and Huan Liu. "Linked Causal Variational Autoencoder for Inferring Paired Spillover Effects." CIKM 2018.	Python
Method-of-moments Estimators	Li, Wenrui, Daniel L. Sussman, and Eric D. Kolaczyk. "Causal Inference under Network Interference with Noise." arXiv preprint arXiv:2105.04518 (2021).	code

Causal Machine Learning

Surveys

Name	Paper	Code
CausalML	Jean Kaddour, Aengus Lynch, Qi Liu, Matt J. Kusner, Ricardo Silva. "Causal Machine Learning: A Survey and Open Problems" arXiv preprint arXiv:2206.15475 (2022).	NA

OoD Generalization

Name	Paper	Code
DomainBed	Gulrajani, Ishaan, and David Lopez-Paz. "In Search of Lost Domain Generalization." In International Conference on Learning Representations. 2020.	code
WILDS	Koh, Pang Wei, Shiori Sagawa, Henrik Marklund, Sang Michael Xie, Marvin Zhang, Akshay Balsubramani, Weihua Hu et al. "Wilds: A benchmark of in-the-wild distribution shifts." In International Conference on Machine Learning, pp. 5637-5664. PMLR, 2021.	code
IBM OoD	Repository for theory and methods for Out-of-Distribution (OoD) generalization by IBM Research	code
OoD Bench	Ye, Nanyang, Kaican Li, Lanqing Hong, Haoyue Bai, Yiting Chen, Fengwei Zhou, and Zhenguo Li. "Ood-bench: Benchmarking and understanding out-of-distribution generalization datasets and algorithms." arXiv preprint arXiv:2106.03721 (2021).	code
BEDS-Bench	Avati, Anand, Martin Seneviratne, Emily Xue, Zhen Xu, Balaji Lakshminarayanan, and Andrew M. Dai. "BEDS-Bench: Behavior of EHR-models under Distributional Shift--A Benchmark." arXiv preprint arXiv:2107.08189 (2021).	code
Survey THU	Shen, Zheyan, Jiashuo Liu, Yue He, Xingxuan Zhang, Renzhe Xu, Han Yu, and Peng Cui. "Towards out-of-distribution generalization: A survey." arXiv preprint arXiv:2108.13624 (2021).	NA

Graph OoD Generalization

Name	Paper	Code
CIGA	Chen, Yongqiang, Yonggang Zhang, Yatao Bian, Han Yang, Kaili Ma, Binghui Xie, Tongliang Liu, Bo Han, and James Cheng. "Learning Causally Invariant Representations for Out-of-Distribution Generalization on Graphs." In Advances in Neural Information Processing Systems (2022).	code
Survey THU	Li, Haoyang, Xin Wang, Ziwei Zhang, and Wenwu Zhu. "Out-of-distribution generalization on graphs: A survey." arXiv preprint arXiv:2202.07987 (2022).	NA

Recommendation Systems

Inverse Propensity Scoring / Doubly Robust

Name	Paper	Code
Top-K Off-policy Correction	Chen, Minmin, Alex Beutel, Paul Covington, Sagar Jain, Francois Belletti, and Ed H. Chi. "Top-k off-policy correction for a REINFORCE recommender system." In Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining, pp. 456-464. ACM, 2019.	Python
Unbiased Offline Recommender Learning	Saito, Yuta, Suguru Yaginuma, Yuta Nishino, Hayato Sakata, and Kazuhide Nakata. "Unbiased Recommender Learning from Missing-Not-At-Random Implicit Feedback." In Proceedings of the 13th International Conference on Web Search and Data Mining, pp. 501-509. ACM, 2020.	Python
Unbiased Offline Recommender Evaluation	Yang, Longqi, Yin Cui, Yuan Xuan, Chenyang Wang, Serge Belongie, and Deborah Estrin. "Unbiased offline recommender evaluation for missing-not-at-random implicit feedback." In Proceedings of the 12th ACM Conference on Recommender Systems, pp. 279-287. ACM, 2018.	Python
IPS Estimator	Schnabel, Tobias, Adith Swaminathan, Ashudeep Singh, Navin Chandak, and Thorsten Joachims. "Recommendations as treatments: Debiasing learning and evaluation." arXiv preprint arXiv:1602.05352 (2016).	Python

Hidden Confounding

Name	Paper	Code
Deconfounded Recsys	Wang, Yixin, Dawen Liang, Laurent Charlin, and David M. Blei. "Causal Inference for Recommender Systems." In Proceedings of the Fourteenth ACM Conference on Recommender Systems (2020).	Python

Domain Adaptation

Name	Paper	Code
Causal Embedding for Recommendation	Bonner, Stephen, and Flavian Vasile. "Causal embeddings for recommendation." In Proceedings of the 12th ACM Conference on Recommender Systems, pp. 104-112. ACM, 2018. (BEST PAPER)	Python
Domain Adversarial Matrix Factorization	Saito, Yuta, and Masahiro Nomura. "Towards Resolving Propensity Contradiction in Offline Recommender Learning." In IJCAI 2022	code

Disentanglement

Name	Paper	Code
Causal Embedding for User Interest and Conformity	Zheng, Y., Gao, C., Li, X., He, X., Li, Y., & Jin, D. (2021, April). Disentangling User Interest and Conformity for Recommendation with Causal Embedding. In Proceedings of the Web Conference 2021 (pp. 2980-2991).	Python

Learning to Rank

Name	Paper	Code
Policy-aware Estimator	Oosterhuis, Harrie, and Maarten de Rijke. "Policy-aware unbiased learning to rank for top-k rankings." In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 489-498. 2020.	Python
Heckman^{rank}	Ovaisi, Zohreh, Ragib Ahsan, Yifan Zhang, Kathryn Vasilaky, and Elena Zheleva. "Correcting for Selection Bias in Learning-to-rank Systems." arXiv preprint arXiv:2001.11358 (2020).	code
Unbiased LambdaMart	Hu, Ziniu, Yang Wang, Qu Peng, and Hang Li. "Unbiased LambdaMART: An Unbiased Pairwise Learning-to-Rank Algorithm." In The World Wide Web Conference, pp. 2830-2836. ACM, 2019.	code
IPW_rank and the Dual Learning Algorithm	Qingyao Ai, Keping Bi, Cheng Luo, Jiafeng Guo, W. Bruce Croft. 2018. Unbiased Learning to Rank with Unbiased Propensity Estimation. In Proceedings of SIGIR '18	Python
Propensity SVM-rank	Joachims, Thorsten, Adith Swaminathan, and Tobias Schnabel. "Unbiased learning-to-rank with biased feedback." In Proceedings of the Tenth ACM International Conference on Web Search and Data Mining, pp. 781-789. ACM, 2017. (BEST PAPER)	Python

Off-line Policy Evaluation/Optimization (for Contextual Bandit or RL)

Name	Paper	Code
Optimal Kernel Balancing	Andrew Bennett, Nathan Kallus. "Policy Evaluation with Latent Confounders via Optimal Balance"	Python
BanditNet	Joachims, Thorsten, Adith Swaminathan, and Maarten de Rijke. "Deep learning with logged bandit feedback." (2018).	Python
Counterfactual Risk Minimization (POEM)	Swaminathan, Adith, and Thorsten Joachims. "Counterfactual risk minimization: Learning from logged bandit feedback." In International Conference on Machine Learning, pp. 814-823. 2015.	Python
Self Normalized Estimator	Swaminathan, Adith, and Thorsten Joachims. "The self-normalized estimator for counterfactual learning." In Advances in Neural Information Processing Systems, pp. 3231-3239. 2015.	Python

Natural Language Processing

Name	Paper	Code
A Review of Using Text to Remove Confounding from Causal Estimates	Keith, Katherine A., David Jensen, and Brendan O'Connor. "Text and Causal Inference: A Review of Using Text to Remove Confounding from Causal Estimates." ACL 2020.	NA
Causal Analysis with Lexicons	Pryzant, Reid, Kelly Shen, Dan Jurafsky, and Stefan Wagner. "Deconfounded lexicon induction for interpretable social science." NAACL 2018.	Python
Causal Text Embeddings	Veitch, Victor, Dhanya Sridhar, and David M. Blei. "Using Text Embeddings for Causal Inference." arXiv preprint arXiv:1905.12741 (2019).	Python
Handling Missing/Noisy Treatment	Wood-Doughty, Zach, Ilya Shpitser, and Mark Dredze. "Challenges of Using Text Classifiers for Causal Inference." In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp. 4586-4598. 2018.	Python
Causal Inferences Using Texts	Egami, Naoki, Christian J. Fong, Justin Grimmer, Margaret E. Roberts, and Brandon M. Stewart. "How to make causal inferences using texts." arXiv preprint arXiv:1802.02163 (2018).	NA

Counterfactual Explanations

Paper	Code
Mothilal, Ramaravind Kommiya, Amit Sharma, and Chenhao Tan. "Explaining machine learning classifiers through diverse counterfactual explanations." arXiv preprint arXiv:1905.07697 (2019).	Python
Russell, Chris. "Efficient search for diverse coherent explanations." In Proceedings of the Conference on Fairness, Accountability, and Transparency, pp. 20-28. 2019.	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 Fairness

Paper	Code
Kusner, Matt J., Joshua Loftus, Chris Russell, and Ricardo Silva. "Counterfactual fairness." In Advances in Neural Information Processing Systems, pp. 4066-4076. 2017.	Python
Wu, Yongkai, Lu Zhang, Xintao Wu, and Hanghang Tong. "Pc-fairness: A unified framework for measuring causality-based fairness." Advances in Neural Information Processing Systems 32 (2019).	code
Chiappa, Silvia. "Path-specific counterfactual fairness." In Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 7801-7808. 2019.	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.	code

Reinforcement Learning

Name	Paper	Code
Deconfounded RL	Lu, Chaochao, Bernhard Schölkopf, and José Miguel Hernández-Lobato. "Deconfounding reinforcement learning in observational settings." arXiv preprint arXiv:1812.10576 (2018).	Python
	Vansteelandt, Stijn, and Marshall Joffe. "Structural nested models and G-estimation: the partially realized promise." Statistical Science 29, no. 4 (2014): 707-731.	NA
Counterfactual-Guided Policy Search (CF-GPS)	Buesing, Lars, Theophane Weber, Yori Zwols, Sebastien Racaniere, Arthur Guez, Jean-Baptiste Lespiau, and Nicolas Heess. "Woulda, Coulda, Shoulda: Counterfactually-Guided Policy Search." arXiv preprint arXiv:1811.06272 (2018).	NA

Multi-Armed Bandit/Causal Bandit

Name	Paper	Code
Causal Bandits	Lattimore, Finnian, Tor Lattimore, and Mark D. Reid. "Causal bandits: Learning good interventions via causal inference." In Advances in Neural Information Processing Systems, pp. 1181-1189. 2016.	NA
Offline+MAB	Ye, Li, Yishi Lin, Hong Xie, and John Lui. "Combining Offline Causal Inference and Online Bandit Learning for Data Driven Decisions." arXiv preprint arXiv:2001.05699 (2020).	NA
B-kl-UCB, B-TS	Zhang, Junzhe, and Elias Bareinboim. "Transfer learning in multi-armed bandit: a causal approach." In Proceedings of the 16th Conference on Autonomous Agents and MultiAgent Systems, pp. 1778-1780. 2017.	NA
Incremental Model	Sawant, Neela, Chitti Babu Namballa, Narayanan Sadagopan, and Houssam Nassif. "Contextual Multi-Armed Bandits for Causal Marketing." arXiv preprint arXiv:1810.01859 (2018).	NA

Causal Discovery

for i.i.d. Data

Classic Methods

Name	Paper	Code
IC algorithm		Python
PC algorithm	P. Spirtes, C. Glymour, and R. Scheines. Causation, Prediction, and Search. The MIT Press, 2nd edition, 2000.	Python R Julia
FCI algorithm	P. Spirtes, C. Glymour, and R. Scheines. Causation, Prediction, and Search. The MIT Press, 2nd edition, 2000.	R Julia

Continuous Optimization

Name	Paper	Code
DAGs with NO TEARS	Zheng, Xun, Bryon Aragam, Pradeep K. Ravikumar, and Eric P. Xing. "DAGs with NO TEARS: Continuous optimization for structure learning." NeurIPS 2018.	Numpy, Pytorch
DAG-GNN	{Yue Yu, Jie Chen, Tian Gao, and Mo Yu. "DAG-GNN: DAG Structure Learning with Graph Neural Networks." ICML 2019.	Pytorch

Amortized Causal Discovery

Name	Paper	Code

Learning IV

Name	Paper	Code
IV Discovery Methods	Silva, Ricardo, and Shohei Shimizu. "Learning instrumental variables with structural and non-gaussianity assumptions." The Journal of Machine Learning Research 18, no. 1 (2017): 4321-4369.	code

Distinguishing Cause from Effect (Bivariate)

Name	Paper	Code
BMLiNGAM	S. Shimizu and K. Bollen. Bayesian estimation of causal direction in acyclic structural equation models with individual-specific confounder variables and non-Gaussian distributions. Journal of Machine Learning Research, 15: 2629-2652, 2014.	Python
Sloppy	Marx, A & Vreeken, J Identifiability of Cause and Effect using Regularized Regression. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), ACM, 2019.	R
RECI	Blöbaum, Patrick, Dominik Janzing, Takashi Washio, Shohei Shimizu, and Bernhard Schölkopf. "Cause-effect inference by comparing regression errors." In International Conference on Artificial Intelligence and Statistics, pp. 900-909. PMLR, 2018.	in CausalDiscoveryToolbox
bQCD	Tagasovska, Natasa, Valérie Chavez-Demoulin, and Thibault Vatter. "Distinguishing cause from effect using quantiles: Bivariate quantile causal discovery." In International Conference on Machine Learning, pp. 9311-9323. PMLR, 2020.	code
CGNN	Goudet, Olivier, Diviyan Kalainathan, Philippe Caillou, Isabelle Guyon, David Lopez-Paz, and Michele Sebag. "Learning functional causal models with generative neural networks." In Explainable and interpretable models in computer vision and machine learning, pp. 39-80. Springer, Cham, 2018.	code

Conditional Independence Tests (for Constraint-based Algorithms)

Name	Paper	Code
RCIT		R

Causal Discovery with Probabilistic Logic Programming

Name	Paper	Code
Causal PSL	Sridhar, Dhanya, Jay Pujara, and Lise Getoor. "Scalable Probabilistic Causal Structure Discovery." In IJCAI, pp. 5112-5118. 2018.	Java

Scalable Ensemble Causal Discovery

Name	Paper	Code
Scalable and Hybrid Ensemble-Based Causality Discovery	Pei Guo, Achuna Ofonedu, Jianwu Wang. "Scalable and Hybrid Ensemble-Based Causality Discovery." In Proceedings of the 2020 IEEE International Conference on Smart Data Services (SMDS), pp. 72-80.	Python

with Temporal Data

Name	Paper	Code
TCDF: Temporal Causal Discovery Framework	Nauta, Meike, Doina Bucur, and Christin Seifert. "Causal discovery with attention-based convolutional neural networks." Machine Learning and Knowledge Extraction.	Pytorch

rguo12/awesome-causality-algorithms

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Repository Details

awesome-causality-algorithms

Table of Contents

Toolboxes

Comprehensive

Treatment Effect Estimation / Uplift Modeling

Causal Discovery

Other Applications

Causal Effect Estimation

With i.i.d Data

Individual Treatment Effect (ITE) / Conditional Average Treatment Effect (CATE)

Deep Learning based methods

Classic Methods

Tree based Methods

Meta Learner

Average Treatment Effect (including ATT and ATC)

Instrumental Variable (IV)

Does-Response Curve (Continuous Treatment)

Vectorized Treatments

Multiple Causes

Multiple Outcomes

Non-i.i.d Data

Panel Data / Time Series

Network Data (with or without Interference)

Causal Machine Learning

Surveys

OoD Generalization

Graph OoD Generalization

Recommendation Systems

Inverse Propensity Scoring / Doubly Robust

Hidden Confounding

Domain Adaptation

Disentanglement

Learning to Rank

Off-line Policy Evaluation/Optimization (for Contextual Bandit or RL)

Natural Language Processing

Counterfactual Explanations

Counterfactual Fairness

Reinforcement Learning

Multi-Armed Bandit/Causal Bandit

Causal Discovery

for i.i.d. Data

Classic Methods

Continuous Optimization

Amortized Causal Discovery

Learning IV

Distinguishing Cause from Effect (Bivariate)

Conditional Independence Tests (for Constraint-based Algorithms)

Causal Discovery with Probabilistic Logic Programming

Scalable Ensemble Causal Discovery

with Temporal Data

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