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https://github.com/rguo12/awesome-causality-algorithms

An index of algorithms for learning causality with data
https://github.com/rguo12/awesome-causality-algorithms

List: awesome-causality-algorithms

awesome baselines causal-inference causality causality-algorithms causality-analysis learning-to-rank multilabel-classification recommender-system unconfoundedness-assumption

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An index of algorithms for learning causality with data

Awesome Lists containing this project

README 

        
# awesome-causality-algorithms [![Awesome](https://awesome.re/badge.svg)](https://awesome.re)



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](https://arxiv.org/pdf/1809.09337) 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](#toolboxes)

- [Causal Effect Estimation](#causal-effect-estimation)

- [Causal Machine Learning](#causal-machine-learning)



## Toolboxes



### Comprehensive

|Name|Code|Comment|

|---|---|---|

|Trustworthy AI|[Python](https://github.com/huawei-noah/trustworthyAI)|Causal Structure Learning, Causal Disentangled Representation Learning, gCastle (or pyCastle, pCastle).|

|[YLearn](https://ylearn.readthedocs.io/en/latest/)|[Python](https://github.com/DataCanvasIO/YLearn)|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](http://causalinference.gitlab.io/kdd-tutorial/)|KDD 2018|[Python](https://github.com/microsoft/dowhy)|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](https://causal-machine-learning.github.io/kdd2021-tutorial/)|KDD 2021|[Python](https://econml.azurewebsites.net/spec/spec.html)|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](https://arxiv.org/pdf/2002.11631.pdf)|arxiv|[Python](https://github.com/uber/causalml)|Uplift modeling and causal inference with machine learning algorithms|

|JustCause|[Underlying thesis](https://justcause.readthedocs.io/en/latest/_downloads/e054f7a0fc9cf9e680173600cb4b4350/thesis-mfranz.pdf)|NA|[Python](https://github.com/inovex/justcause)|For evaluation of heterogeneous treatment effect estimators on common reference as well as synthetic data.|

|WhyNot|[Documentation](https://whynot.readthedocs.io/en/latest/)|NA|[Python](https://github.com/zykls/whynot)|An experimental sandbox for causal inference and decision making in dynamics.|

|scikit-uplift|[Documentation](https://www.uplift-modeling.com/en/latest/index.html) and [User guide for uplift modeling](https://www.uplift-modeling.com/en/latest/user_guide/index.html)|NA|[Python](https://github.com/maks-sh/scikit-uplift)|Uplift modeling in scikit-learn style in python. |



### Causal Discovery

|Name|Paper|Code|Comment|

|---|---|---|---|

|[Bench Press](https://benchpressdocs.readthedocs.io/en/latest/)|[Benchpress: a scalable and versatile workflow for benchmarking structure learning algorithms for graphical models](https://arxiv.org/abs/2107.03863)|[Code](https://github.com/felixleopoldo/benchpress)|Reproducible and scalable execution and benchmarks of **41** structure learning algorithms supporting multiple language|

|[causal-learn](https://causal-learn.readthedocs.io/en/latest/)|NA|[Python](https://github.com/cmu-phil/causal-learn)|Causal Discovery for Python. A translation and extension of TETRAD.|

|[TETRAD R/Java](http://www.phil.cmu.edu/tetrad/about.html)|[TETRAD-A Toolbox FOR CAUSAL DISCOVERY](https://www.atmos.colostate.edu/~iebert/PAPERS/CI2018_paper_35.pdf)|[R](https://github.com/bd2kccd/r-causal)/[Java](https://github.com/cmu-phil/tetrad)|Causal Discovery Toolbox from CMU|

|Causaldag|NA|[code](https://github.com/uhlerlab/causaldag)|Python package for the creation, manipulation, and learning of Causal DAGs|

|CausalNex|NA|[Python](https://github.com/quantumblacklabs/causalnex)|A toolkit for causal reasoning with Bayesian Networks.|

|CausalDiscoveryToolbox|[Causal Discovery Toolbox: Uncover causal relationships in Python](https://arxiv.org/pdf/1903.02278)|[Python](https://github.com/Diviyan-Kalainathan/CausalDiscoveryToolbox)||



### Rootcause Analysis

|Name|Paper|Code|Comments|

|---|---|---|---|

|[Chaos Genius](https://www.chaosgenius.io/)|NA|[Python](https://github.com/chaos-genius/chaos_genius/)|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](https://arxiv.org/abs/2002.02770)|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](https://arxiv.org/pdf/1606.03976)|ICML 2017|[Python](https://github.com/oddrose/cfrnet)|

|BNN, BLR|[Learning representations for counterfactual inference](http://www.jmlr.org/proceedings/papers/v48/johansson16.pdf)|ICML 2016|[Python](https://github.com/oddrose/cfrnet)|

|Causal Effect VAE|[Causal effect inference with deep latent-variable models](http://papers.nips.cc/paper/7223-causal-effect-inference-with-deep-latent-variable-models.pdf)|Neurips 2017|[Python](https://github.com/AMLab-Amsterdam/CEVAE)|

|Dragonnet|[Adapting neural networks for the estimation of treatment effects.](https://arxiv.org/abs/1906.02120)|Neurips 2019|[Python](https://github.com/claudiashi57/dragonnet)|

|SITE|[Representation Learning for Treatment Effect Estimation from Observational Data](https://papers.nips.cc/paper/7529-representation-learning-for-treatment-effect-estimation-from-observational-data.pdf)|Neurips 2018|[Python](https://github.com/Osier-Yi/SITE)|

|GANITE|[GANITE: Estimation of Individualized Treatment Effects using Generative Adversarial Nets](https://openreview.net/forum?id=ByKWUeWA-)|ICLR 2018|[Python](https://github.com/jsyoon0823/GANITE)|

|Perfect Match|[Perfect match: A simple method for learning representations for counterfactual inference with neural networks](https://arxiv.org/pdf/1810.00656)|arxiv|[Python](https://github.com/d909b/perfect_match)|

|Intact-VAE|Intact-VAE: Estimating treatment effects under unobserved confounding|ICLR 2022|[code](https://openreview.net/forum?id=q7n2RngwOM)|

|CausalEGM|[CausalEGM: a general causal inference framework by encoding generative modeling](https://arxiv.org/abs/2212.05925)|arxiv|[Python](https://github.com/SUwonglab/CausalEGM)|



##### 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.](https://academic.oup.com/biomet/article-pdf/70/1/41/662954/70-1-41.pdf)|[Python](https://github.com/akelleh/causality/tree/master/causality/estimation)|



 

##### 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).](https://www.tandfonline.com/doi/pdf/10.1080/01621459.2017.1319839)|[code R](https://github.com/grf-labs/grf), [code Python](https://github.com/kjung/scikit-learn)|

|Causal MARS, Causal Boosting, Pollinated Transformed Outcome Forests|[S. Powers et al., “Some methods for heterogeneous treatment effect estimation in high-dimensions,” 2017.](https://arxiv.org/pdf/1707.00102.pdf)|[code R](https://github.com/grf-labs/grf), [code R](https://github.com/saberpowers/causalLearning)|

|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.](https://www.tandfonline.com/doi/pdf/10.1198/jcgs.2010.08162)|[Python](https://github.com/JakeColtman/bartpy)|



##### 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.](https://www.pnas.org/content/pnas/early/2019/02/14/1804597116.full.pdf)|[code R](https://github.com/soerenkuenzel/hte), [code R](https://github.com/xnie/rlearner/blob/master/R/xlearner.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.](https://academic.oup.com/biomet/article-pdf/70/1/41/662954/70-1-41.pdf)|[R](https://github.com/cran/ipw)|

|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.](https://onlinelibrary.wiley.com/doi/full/10.1111/j.1541-0420.2005.00377.x)|[R](https://github.com/gregridgeway/fastDR)|

|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).](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6347556/)|[R](https://github.com/jantonelli111/DoublyRobustHD)|

|TMLE|[Gruber, Susan, and Mark J. van der Laan. "tmle: An R package for targeted maximum likelihood estimation." (2011).](https://www.jstatsoft.org/article/view/v051i13)|[R](https://cran.r-project.org/web/packages/tmle/index.html)|

|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.](https://www.jstor.org/stable/pdf/41403737.pdf)|[R](https://github.com/cran/ebal)|

|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.](https://rss.onlinelibrary.wiley.com/doi/full/10.1111/rssb.12027)|[R](https://github.com/kosukeimai/CBPS)|

|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.](https://rss.onlinelibrary.wiley.com/doi/pdf/10.1111/rssb.12268)|[R](https://github.com/swager/balanceHD)|

|Causal Bootstrapping|Little, Max A., and Reham Badawy. "Causal bootstrapping." arXiv preprint arXiv:1910.09648 (2019).|[Matlab](http://www.maxlittle.net/software/cblib.zip)|



#### 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.](http://proceedings.mlr.press/v70/hartford17a/hartford17a.pdf)|[Python](https://github.com/jhartford/DeepIV)|

|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.](https://ideas.repec.org/c/boc/bocode/s458459.html)|[STATA](https://statalasso.github.io/)|



#### 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](https://joss.theoj.org/papers/10.21105/joss.02523)|[Python](https://github.com/ronikobrosly/causal-curve)|

|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).](https://arxiv.org/pdf/1902.00981.pdf)|[Python](https://github.com/d909b/drnet)|



#### 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.](https://arxiv.org/pdf/2106.01939.pdf)|[Python](https://github.com/JeanKaddour/SIN)|



#### Multiple Causes

|Name|Paper|Code|

|---|---|---|

|Deconfounder|[Wang, Yixin, and David M. Blei. "The blessings of multiple causes." arXiv preprint arXiv:1805.06826 (2018).](https://arxiv.org/abs/1805.06826)|[Python](https://github.com/blei-lab/deconfounder_tutorial)|



#### Multiple Outcomes

|Name|Paper|Code|

|---|---|---|

|Multiple Responses in Uplift Models|[Weiss, Sam. Estimating and Visualizing Business Tradeoffs in Uplift Models](https://medium.com/building-ibotta/estimating-and-visualizing-business-tradeoffs-in-uplift-models-80ff845a5698) |[Python](https://github.com/Ibotta/mr_uplift)|



### 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.](https://www.aeaweb.org/articles?id=10.1257/jel.20191450)|[R](https://cran.r-project.org/web/packages/Synth/Synth.pdf)|

|Synthetic Difference in Differences|[Arkhangelsky, Dmitry, et al. Synthetic difference in differences. No. w25532. National Bureau of Economic Research, 2019.](https://www.nber.org/system/files/working_papers/w25532/w25532.pdf)|[R](https://github.com/synth-inference/synthdid)
[Python](https://github.com/MasaAsami/pysynthdid)|

|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](https://projecteuclid.org/journals/annals-of-applied-statistics/volume-9/issue-1/Inferring-causal-impact-using-Bayesian-structural-time-series-models/10.1214/14-AOAS788.full)|[R](https://github.com/cran/bsts)
[Python](https://github.com/WillianFuks/tfcausalimpact)|

|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.](https://arxiv.org/pdf/1902.00450.pdf)|[code](https://github.com/ioanabica/Time-Series-Deconfounder)|

|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.](http://medianetlab.ee.ucla.edu/papers/nips_rmsn.pdf)|[Python](https://github.com/sjblim/rmsn_nips_2018)|

|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.](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4405134/)|[R](https://github.com/joshuaschwab/ltmle)|

|Causal Transformer|[Melnychuk, Valentyn, Dennis Frauen, and Stefan Feuerriegel. "Causal Transformer for Estimating Counterfactual Outcomes." arXiv preprint arXiv:2204.07258 (2022).](https://arxiv.org/abs/2204.07258)|[Python](https://github.com/Valentyn1997/CausalTransformer)|



#### 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.](https://arxiv.org/abs/1906.03485)|[Python](https://github.com/rguo12/network-deconfounder-wsdm20)|

|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).](https://arxiv.org/pdf/1902.04114)|[Python](https://github.com/vveitch/causal-network-embeddings)|

|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.](http://www.public.asu.edu/~rguo12/CIKM18.pdf)|[Python](https://github.com/rguo12/CIKM18-LCVA)|

|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).](https://arxiv.org/abs/2105.04518)|[code](https://github.com/KolaczykResearch/CausInfNoisyNet)|



## 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)](https://arxiv.org/abs/2206.15475).|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](https://github.com/facebookresearch/DomainBed)|

|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](https://github.com/p-lambda/wilds)|

|IBM OoD|Repository for theory and methods for Out-of-Distribution (OoD) generalization by IBM Research|[code](https://github.com/IBM/OoD)|

|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](https://github.com/m-Just/OoD-Bench)|

|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](https://github.com/Google-Health/records-research/tree/master/beds-bench)|

|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](https://github.com/LFhase/CIGA)|

|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.](https://arxiv.org/abs/1812.02353)|[Python](https://github.com/awarebayes/RecNN)|

|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.](https://dl.acm.org/doi/abs/10.1145/3336191.3371783)|[Python](https://github.com/usaito/unbiased-implicit-rec-real)|

|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.](https://dl.acm.org/citation.cfm?id=3240355)|[Python](https://github.com/ylongqi/unbiased-offline-recommender-evaluation)|

|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).](http://www.jmlr.org/proceedings/papers/v48/schnabel16.pdf)|[Python](http://www.cs.cornell.edu/~schnabts/mnar/index.html)|



#### 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).](https://dl.acm.org/doi/10.1145/3383313.3412225)|[Python](https://github.com/yixinwang/causal-recsys-public)|



#### 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**)](https://arxiv.org/pdf/1706.07639)|[Python](https://github.com/criteo-research/CausE)|

|Domain Adversarial Matrix Factorization|[Saito, Yuta, and Masahiro Nomura. "Towards Resolving Propensity Contradiction in Offline Recommender Learning." In IJCAI 2022](https://usaito.github.io/files/IJCAI2022_DAMF.pdf)|[code](https://github.com/usaito/ijcai2022-adversarial-mf)|



#### 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).](https://arxiv.org/abs/2006.11011)|[Python](https://github.com/tsinghua-fib-lab/DICE)|



### 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.](https://arxiv.org/pdf/2005.09035.pdf)|[Python](https://github.com/HarrieO/2020topkunbiasedltr)|

|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).](https://arxiv.org/abs/2001.11358)|[code](https://github.com/edgeslab/heckman_rank)|

|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.](https://acbull.github.io/pdf/unbias.pdf)|[code](https://github.com/acbull/Unbiased_LambdaMart)|

|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](https://arxiv.org/abs/1804.05938)|[Python](https://github.com/QingyaoAi/Unbiased-Learning-to-Rank-with-Unbiased-Propensity-Estimation)|

|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**)](https://dl.acm.org/ft_gateway.cfm?id=3018699&type=pdf)|[Python](http://www.cs.cornell.edu/people/tj/svm_light/svm_proprank.html)|



### 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"](http://arxiv.org/abs/1908.01920)|[Python](https://github.com/CausalML/LatentConfounderBalancing)|

|BanditNet|[Joachims, Thorsten, Adith Swaminathan, and Maarten de Rijke. "Deep learning with logged bandit feedback." (2018).](https://openreview.net/pdf?id=SJaP_-xAb)|[Python](https://www.cs.cornell.edu/people/tj/banditnet/index.html)|

|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.](http://www.jmlr.org/proceedings/papers/v37/swaminathan15.pdf)|[Python](http://www.cs.cornell.edu/~adith/POEM/index.html)|

|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.](http://papers.nips.cc/paper/5748-the-self-normalized-estimator-for-counterfactual-learning.pdf)|[Python](http://www.cs.cornell.edu/~adith/POEM/index.html)|



### 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.](https://www.aclweb.org/anthology/2020.acl-main.474.pdf)|NA|

|Causal Analysis with Lexicons|[Pryzant, Reid, Kelly Shen, Dan Jurafsky, and Stefan Wagner. "Deconfounded lexicon induction for interpretable social science." NAACL 2018.](https://www.aclweb.org/anthology/N18-1146.pdf)|[Python](https://github.com/rpryzant/causal_attribution)|

|Causal Text Embeddings|[Veitch, Victor, Dhanya Sridhar, and David M. Blei. "Using Text Embeddings for Causal Inference." arXiv preprint arXiv:1905.12741 (2019).](https://arxiv.org/pdf/1905.12741.pdf)|[Python](https://github.com/blei-lab/causal-text-embeddings)|

|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.](https://aclweb.org/anthology/D18-1488)|[Python](https://github.com/zachwooddoughty/emnlp2018-causal)|

|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).](https://arxiv.org/pdf/1802.02163.pdf)|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).](https://arxiv.org/pdf/1905.07697.pdf)|[Python](https://github.com/microsoft/DiCE)|

|[Russell, Chris. "Efficient search for diverse coherent explanations." In Proceedings of the Conference on Fairness, Accountability, and Transparency, pp. 20-28. 2019.](https://dl.acm.org/doi/pdf/10.1145/3287560.3287569)|[Python](https://bitbucket.org/ChrisRussell/diverse-coherent-explanations/)|

|[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.](https://heinonline.org/hol-cgi-bin/get_pdf.cgi?handle=hein.journals/hjlt31&section=29)||



### 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.](http://papers.nips.cc/paper/6995-counterfactual-fairness.pdf)|[Python](https://github.com/mkusner/counterfactual-fairness)|

|[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).](https://proceedings.neurips.cc/paper/2019/file/44a2e0804995faf8d2e3b084a1e2db1d-Paper.pdf)|[code](https://github.com/yongkaiwu/Path-specific-Counterfactual-Fairness)|

|[Chiappa, Silvia. "Path-specific counterfactual fairness." In Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 7801-7808. 2019.](https://www.aaai.org/ojs/index.php/AAAI/article/download/4777/4655)|[code](https://www.deepmind.com/open-source/path-specific-counterfactual-fairness-in-jax)|

|[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.](https://dl.acm.org/doi/pdf/10.1145/3306618.3317950)|[code](https://github.com/SaiSakethAluru/Counterfactual-fairness)|



### 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).](https://arxiv.org/pdf/1812.10576)|[Python](https://github.com/CausalRL/DRL)|

||[Vansteelandt, Stijn, and Marshall Joffe. "Structural nested models and G-estimation: the partially realized promise." Statistical Science 29, no. 4 (2014): 707-731.](https://arxiv.org/pdf/1503.01589.pdf)|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.](https://arxiv.org/abs/1606.03203)|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).](https://arxiv.org/pdf/2001.05699v1.pdf)|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.](http://www.ifaamas.org/Proceedings/aamas2017/pdfs/p1778.pdf)|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).](https://arxiv.org/pdf/1810.01859)|NA|



## Causal Discovery

### for i.i.d. Data

#### Classic Methods

|Name|Paper|Code|

|---|---|---|

|IC algorithm| |[Python](https://github.com/akelleh/causality)|

|PC algorithm|P. Spirtes, C. Glymour, and R. Scheines. Causation, Prediction, and Search. The MIT Press, 2nd edition, 2000.|[Python](https://github.com/keiichishima/pcalg) [R](https://github.com/cran/pcalg) [Julia](https://github.com/mschauer/CausalInference.jl)|

|FCI algorithm|P. Spirtes, C. Glymour, and R. Scheines. Causation, Prediction, and Search. The MIT Press, 2nd edition, 2000. |[R](https://github.com/cran/pcalg) [Julia](https://github.com/mschauer/CausalInference.jl)|



#### Continuous Optimization

|Paper|Venue|Code|

|---|---|---|

|[DAGs with NO TEARS: Continuous optimization for structure learning](http://papers.nips.cc/paper/8157-dags-with-no-tears-continuous-optimization-for-structure-learning)|NeurIPS 2018|[code](https://github.com/xunzheng/notears)|

|[DAG-GNN: DAG Structure Learning with Graph Neural Networks](https://arxiv.org/abs/1904.10098)|ICML 2019|[code](https://github.com/fishmoon1234/DAG-GNN)|

|[Learning Sparse Nonparametric DAGs](http://proceedings.mlr.press/v108/zheng20a/zheng20a.pdf)|AISTATS 2020|[code](https://github.com/xunzheng/notears)|

|[Amortized Inference for Causal Structure Learning](https://proceedings.neurips.cc/paper_files/paper/2022/file/54f7125dee9b8b3dc798bb9a082b09e2-Paper-Conference.pdf)|Neurips 2022|[code](https://github.com/larslorch/avici)|



#### Learning IV

|Name|Paper|Code|

|---|---|---|

|[Learning instrumental variables with structural and non-gaussianity assumptions](http://www.jmlr.org/papers/volume18/17-014/17-014.pdf)|JMLR|[code](http://www.homepages.ucl.ac.uk/~ucgtrbd/code/iv_discovery/)|



#### 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](https://github.com/taku-y/bmlingam)|

|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](https://eda.mmci.uni-saarland.de/prj/sloppy/)|

|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](https://github.com/FenTechSolutions/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](https://github.com/tagas/bQCD)|

|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](https://github.com/GoudetOlivier/CGNN)|



#### Conditional Independence Tests (for Constraint-based Algorithms)

|Name|Paper|Code|

|---|---|---|

|RCIT|   |[R](https://github.com/ericstrobl/RCIT)|



#### 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](https://bitbucket.org/linqs/causpsl)|



#### 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.](https://ieeexplore.ieee.org/document/9288491)|[Python](https://github.com/big-data-lab-umbc/ensemble_causality_learning)|



### 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.](https://www.mdpi.com/2504-4990/1/1/19)|[Pytorch](https://github.com/M-Nauta/TCDF)|