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}
}

Table of Contents

• Toolboxes
• Causal Effect Estimation
• Causal Machine Learning

Toolboxes

Comprehensive

Name	Code	Comment
Trustworthy AI	Python	Causal Structure Learning, Causal Disentangled Representation Learning, gCastle (or pyCastle, pCastle).
YLearn	Python	Python package for causal discovery，causal effect identification/estimation, counterfactual inference，policy learning，etc.

Treatment Effect Estimation / Uplift Modeling

Name	Paper/Documentation	Venue	Code	Comment
DoWhy	Tutorial on Causal Inference and Counterfactual Reasoning	KDD 2018	Python	Python library for causal inference that supports explicit modeling and testing of causal assumptions.
EconML	Causal Inference and Machine Learning in Practice with EconML and CausalML	KDD 2021	Python	Python package that applies the power of machine learning techniques to estimate individualized causal responses from observational or experimental data.
CausalML	Causalml: Python package for causal machine learning	arxiv	Python	Uplift modeling and causal inference with machine learning algorithms
JustCause	Underlying thesis	NA	Python	For evaluation of heterogeneous treatment effect estimators on common reference as well as synthetic data.
WhyNot	Documentation	NA	Python	An experimental sandbox for causal inference and decision making in dynamics.
scikit-uplift	Documentation and User guide for uplift modeling	NA	Python	Uplift modeling in scikit-learn style in python.

Causal Discovery

Name	Paper	Code	Comment
Bench Press	Benchpress: a scalable and versatile workflow for benchmarking structure learning algorithms for graphical models	Code	Reproducible and scalable execution and benchmarks of 41 structure learning algorithms supporting multiple language
causal-learn	NA	Python	Causal Discovery for Python. A translation and extension of TETRAD.
TETRAD R/Java	TETRAD-A Toolbox FOR CAUSAL DISCOVERY	R/Java	Causal Discovery Toolbox from CMU
Causaldag	NA	code	Python package for the creation, manipulation, and learning of Causal DAGs
CausalNex	NA	Python	A toolkit for causal reasoning with Bayesian Networks.
CausalDiscoveryToolbox	Causal Discovery Toolbox: Uncover causal relationships in Python	Python

Rootcause Analysis

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

Causal Effect Estimation

Survey Papers

Name	Paper	Venue
	A survey on causal inference	TKDD

With i.i.d Data

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

Deep Learning based methods

Name	Paper	Venue	Code
TARNet, Counterfactual Regression	Estimating individual treatment effect: generalization bounds and algorithms	ICML 2017	Python
BNN, BLR	Learning representations for counterfactual inference	ICML 2016	Python
Causal Effect VAE	Causal effect inference with deep latent-variable models	Neurips 2017	Python
Dragonnet	Adapting neural networks for the estimation of treatment effects.	Neurips 2019	Python
SITE	Representation Learning for Treatment Effect Estimation from Observational Data	Neurips 2018	Python
GANITE	GANITE: Estimation of Individualized Treatment Effects using Generative Adversarial Nets	ICLR 2018	Python
Perfect Match	Perfect match: A simple method for learning representations for counterfactual inference with neural networks	arxiv	Python
Intact-VAE	Intact-VAE: Estimating treatment effects under unobserved confounding	ICLR 2022	code
CausalEGM	CausalEGM: a general causal inference framework by encoding generative modeling	arxiv	Python
<!--	BNR-NNM(balanced and nonlinear representations-nearest neighbor matching)	Li, Sheng, and Yun Fu. "Matching on balanced nonlinear representations for treatment effects estimation." In Advances in Neural Information Processing Systems, pp. 929-939. 2017.	NA
<!--	Deep Counterfactual Networks (Propensity Dropout)	Alaa, Ahmed M., Michael Weisz, and Mihaela van der Schaar. "Deep counterfactual networks with propensity-dropout." arXiv preprint arXiv:1706.05966 (2017)	NA

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
<!--	Nonparametric Regression Adjustment
-->

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
<!--	Active Learning for Decision-Making from Imbalanced Observational Data	Active Learning for Decision-Making from Imbalanced Observational Data
<!--	ABCEI	Adversarial Balancing-based Representation Learning for Causal Effect Inference with Observational Data
<!--	NSGP (Non-stationary Gaussian Process Prior)	Alaa, Ahmed, and Mihaela Schaar. "Limits of estimating heterogeneous treatment effects: Guidelines for practical algorithm design." In International Conference on Machine Learning, pp. 129-138. 2018.
<!--	CMGP (Causal Multi-task Gaussian Processes)	Alaa, Ahmed M., and Mihaela van der Schaar. "Bayesian inference of individualized treatment effects using multi-task gaussian processes." In Advances in Neural Information Processing Systems, pp. 3424-3432. 2017.
<!--	Functional Interval Estimator	Kallus, Nathan, Xiaojie Mao, and Angela Zhou. "Interval Estimation of Individual-Level Causal Effects Under Unobserved Confounding." In The 22nd International Conference on Artificial Intelligence and Statistics, pp. 2281-2290. 2019.

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
<!--	Differentiated Confounder Balancing	Kuang, Kun, Peng Cui, Bo Li, Meng Jiang, and Shiqiang Yang. "Estimating Treatment Effect in the Wild via Differentiated Confounder Balancing." In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 265-274. ACM, 2017.
<!--	Adversarial Balancing	Ozery-Flato, Michal, Pierre Thodoroff, and Tal El-Hay. "Adversarial Balancing for Causal Inference." arXiv preprint arXiv:1810.07406 (2018).
<!--	DeepMatch	Kallus, Nathan. "Deepmatch: Balancing deep covariate representations for causal inference using adversarial training." arXiv preprint arXiv:1802.05664 (2018).

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
<!--		Imai, Kosuke, and Zhichao Jiang. "Discussion of "The Blessings of Multiple Causes" by Wang and Blei."
	D'Amour, Alexander. "On multi-cause causal inference with unobserved confounding: Counterexamples, impossibility, and alternatives." arXiv preprint arXiv:1902.10286 (2019).	NA
	Ranganath, Rajesh, and Adler Perotte. "Multiple causal inference with latent confounding." arXiv preprint arXiv:1805.08273 (2018).	NA
	Kong, Dehan, Shu Yang, and Linbo Wang. "Multi-cause causal inference with unmeasured confounding and binary outcome." arXiv preprint arXiv:1907.13323 (2019).	NA
	Elizabeth L. Ogburn, Ilya Shpitser, Eric J. Tchetgen Tchetgen "Comment on Blessings of Multiple Causes." arXiv preprint arXiv:1910.05438 (2019)	NA

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
<!--	GNN-based Causal Effect Estimators	Ma, Yunpu, Yuyi Wang, and Volker Tresp. "Causal Inference under Networked Interference." arXiv preprint arXiv:2002.08506 (2020).

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
<!--	Doubly Robust Joint Learning	Wang, Xiaojie, Rui Zhang, Yu Sun, and Jianzhong Qi. "Doubly Robust Joint Learning for Recommendation on Data Missing Not at Random." In International Conference on Machine Learning, pp. 6638-6647. 2019.

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
<!--	Regression EM	Wang, Xuanhui, Nadav Golbandi, Michael Bendersky, Donald Metzler, and Marc Najork. "Position bias estimation for unbiased learning to rank in personal search." In Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining, pp. 610-618. ACM, 2018.
<!--	Various Bias Models	Wang, Xuanhui, Michael Bendersky, Donald Metzler, and Marc Najork. "Learning to rank with selection bias in personal search." In Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval, pp. 115-124. ACM, 2016.
<!--	TrustPBM	Agarwal, Aman, Xuanhui Wang, Cheng Li, Mike Bendersky, and Marc Najork. "Addressing Trust Bias for Unbiased Learning-to-Rank." In The World Wide Web Conference, pp. 4-14. ACM, 2019.
<!--	Intervention Harvesting	Agarwal, Aman, Ivan Zaitsev, Xuanhui Wang, Cheng Li, Marc Najork, and Thorsten Joachims. "Estimating Position Bias without Intrusive Interventions." In Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining, pp. 474-482. ACM, 2019.

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
<!--	Surrogate Policy	Xie, Yuan, Boyi Liu, Qiang Liu, Zhaoran Wang, Yuan Zhou, and Jian Peng. "Off-Policy Evaluation and Learning from Logged Bandit Feedback: Error Reduction via Surrogate Policy." arXiv preprint arXiv:1808.00232 (2018).
<!--	Balanced Policy Evaluation and Learning	Kallus, Nathan. "Balanced policy evaluation and learning." In Advances in Neural Information Processing Systems, pp. 8895-8906. 2018.
<!--	Confounding-robust Policy Learning	Kallus, Nathan, and Angela Zhou. "Confounding-robust policy improvement." In Advances in Neural Information Processing Systems, pp. 9269-9279. 2018.
<!--	Multi-action Policy Learning	Zhou, Zhengyuan, Susan Athey, and Stefan Wager. "Offline multi-action policy learning: Generalization and optimization." arXiv preprint arXiv:1810.04778 (2018).
<!--	Efficient Policy Learning	Athey, Susan, and Stefan Wager. "Efficient policy learning." arXiv preprint arXiv:1702.02896 (2017).
<!--	Focused Context Balancing	Zou, Hao, Kun Kuang, Boqi Chen, Peixuan Chen, and Peng Cui. "Focused Context Balancing for Robust Offline Policy Evaluation." In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 696-704. ACM, 2019.

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
<!--	Causal FS for text classification	Michael J. Paul. Feature selection as causal inference: experiments with text classification. Conference on Computational Natural Language Learning (CoNLL), Vancouver, Canada. August 2017.
<!--	Conditional Treatment-adversarial Learning Based Matching	Yao, Liuyi, Sheng Li, Yaliang Li, Hongfei Xue, Jing Gao, and Aidong Zhang. "On the estimation of treatment effect with text covariates." In Proceedings of the 28th International Joint Conference on Artificial Intelligence, pp. 4106-4113. AAAI Press, 2019.

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
<!--	Russell, Chris, Matt J. Kusner, Joshua Loftus, and Ricardo Silva. "When worlds collide: integrating different counterfactual assumptions in fairness." In Advances in Neural Information Processing Systems, pp. 6414-6423. 2017.

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

Paper	Venue	Code
DAGs with NO TEARS: Continuous optimization for structure learning	NeurIPS 2018	code
DAG-GNN: DAG Structure Learning with Graph Neural Networks	ICML 2019	code
Learning Sparse Nonparametric DAGs	AISTATS 2020	code
Amortized Inference for Causal Structure Learning	Neurips 2022	code

Learning IV

Name	Paper	Code
Learning instrumental variables with structural and non-gaussianity assumptions	JMLR	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