https://github.com/1587causalai/awesome-causality-algorithms

An index of algorithms for learning causality with data

https://github.com/1587causalai/awesome-causality-algorithms

Repository

An index of algorithms for learning causality with data

https://github.com/1587causalai/awesome-causality-algorithms/blob/master/

# awesome-causality-algorithms
An index of algorithms for learning causality with data.

Please cite [our survey paper](https://arxiv.org/pdf/1809.09337) if this index is helpful.
```
@article{guo2018survey,
  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={arXiv preprint arXiv:1809.09337}, 
  year={2018}
}
```
*The survey has been updated! Check it on arxiv!*

*Updates for this repo are coming soon!*

## Learning Causal Effects

### With i.i.d Data

|Name|Paper|Code|
|---|---|---|
|Propensity Score Matching|  |[Python](https://github.com/akelleh/causality/tree/master/causality/estimation)|
|Inverse Probability Reweighting|   |[R](https://github.com/cran/ipw)|
|Nonparametric Regression Adjustment|   |[Python](https://github.com/akelleh/causality)|
|Doubly Robust Estimation|   |[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)|
|---|---|---|
|BNN, BLR|[Johansson, Fredrik, Uri Shalit, and David Sontag. "Learning representations for counterfactual inference." In International Conference on Machine Learning, pp. 3020-3029. 2016.](http://www.jmlr.org/proceedings/papers/v48/johansson16.pdf)|[Python](https://github.com/oddrose/cfrnet)|
|Tarnet, Counterfactual Regression|[Shalit, Uri, Fredrik D. Johansson, and David Sontag. "Estimating individual treatment effect: generalization bounds and algorithms." arXiv preprint arXiv:1606.03976 (2016).](https://arxiv.org/pdf/1606.03976)|[Python](https://github.com/oddrose/cfrnet)|
|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.](http://papers.nips.cc/paper/7223-causal-effect-inference-with-deep-latent-variable-models.pdf)|[Python](https://github.com/AMLab-Amsterdam/CEVAE)|
|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.](https://papers.nips.cc/paper/7529-representation-learning-for-treatment-effect-estimation-from-observational-data.pdf)|[Python](https://github.com/Osier-Yi/SITE)|
|Meta-learners for Estimating Heterogeneous Treatment Effects using Machine Learning|[Knzel, Sren R., Jasjeet S. Sekhon, Peter J. Bickel, and Bin Yu. "Meta-learners for Estimating Heterogeneous Treatment Effects using Machine Learning." arXiv preprint arXiv:1706.03461 (2017).](https://www.pnas.org/content/pnas/early/2019/02/14/1804597116.full.pdf)|[R](https://github.com/soerenkuenzel/hte)|
|Causal Forest|[Wager, Stefan, and Susan Athey. "Estimation and inference of heterogeneous treatment effects using random forests." Journal of the American Statistical Association just-accepted (2017).](https://www.tandfonline.com/doi/pdf/10.1080/01621459.2017.1319839)|[R](https://github.com/grf-labs/grf) [Python](https://github.com/kjung/scikit-learn)|
|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)|
|GANITE|[Yoon, Jinsung, James Jordon, and Mihaela van der Schaar. "GANITE: Estimation of Individualized Treatment Effects using Generative Adversarial Nets." (2018).](https://openreview.net/forum?id=ByKWUeWA-)|[Python](https://github.com/d909b/perfect_match/tree/master/perfect_match/models/baselines/ganite_package)|
|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)](https://arxiv.org/pdf/1810.00656)|[Python](https://github.com/d909b/perfect_match)|
|Active Learning for Decision-Making from Imbalanced Observational Data|[Active Learning for Decision-Making from Imbalanced Observational Data](https://arxiv.org/abs/1904.05268)|NA|
|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.](http://proceedings.mlr.press/v80/alaa18a/alaa18a.pdf)|NA|
|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.](https://papers.nips.cc/paper/6934-bayesian-inference-of-individualized-treatment-effects-using-multi-task-gaussian-processes.pdf)|NA|
|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.](http://papers.nips.cc/paper/6694-matching-on-balanced-nonlinear-representations-for-treatment-effects-estimation.pdf)|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)](https://arxiv.org/pdf/1706.05966)|NA|
|---|---|---|
|For Average Treatment Effect (ATE), ATT, ATC|||
|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.](http://media.cs.tsinghua.edu.cn/~multimedia/cuipeng/papers/CausalDCA.pdf)|NA|
|Adversarial Balancing|[Ozery-Flato, Michal, Pierre Thodoroff, and Tal El-Hay. "Adversarial Balancing for Causal Inference." arXiv preprint arXiv:1810.07406 (2018).](https://arxiv.org/pdf/1810.07406)|NA|
|DeepMatch|[Kallus, Nathan. "Deepmatch: Balancing deep covariate representations for causal inference using adversarial training." arXiv preprint arXiv:1802.05664 (2018).](https://arxiv.org/pdf/1802.05664)|NA|

#### Treatment Responder Classification
|Name|Paper|Code|
|---|---|---|
|RespSVM|[Kallus, Nathan. "Classifying Treatment Responders Under Causal Effect Monotonicity." arXiv preprint arXiv:1902.05482 (2019)](https://arxiv.org/pdf/1902.05482.pdf)|NA|

#### Learning Causal Effects as Response Curves
|Name|Paper|Code|
|---|---|---|
|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)|

#### Learning Causal Effects with Multi-cause Data
|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)|
||[Ranganath, Rajesh, and Adler Perotte. "Multiple causal inference with latent confounding." arXiv preprint arXiv:1805.08273 (2018).](https://arxiv.org/pdf/1805.08273)|NA|

#### Transfer Learning for Learning Causal Effects
|Name|Paper|Code|
|---|---|---|
|The Y-learner|[Knzel, Sren R., Bradly C. Stadie, Nikita Vemuri, Varsha Ramakrishnan, Jasjeet S. Sekhon, and Pieter Abbeel. "Transfer Learning for Estimating Causal Effects using Neural Networks." arXiv preprint arXiv:1808.07804 (2018).](https://arxiv.org/pdf/1808.07804.pdf)|NA|

#### Variable Selection/Importance for Learning Causal Effects
|Name|Paper|Code|
|---|---|---|
|Variable importance through targeted causal inference|[The Github Repo "varimpact" by Alan E. Hubbard and Chris J. Kennedy, University of California, Berkeley](https://github.com/ck37/varimpact)|[R](https://github.com/ck37/varimpact)|

#### Instrumental Variables
|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/)|

### Non-i.i.d Data
#### Learning Causal Effects under Spillover Effect
|Name|Paper|Code|
|---|---|---|
|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." In Proceedings of the 27th ACM International Conference on Information and Knowledge Management, pp. 1679-1682. ACM, 2018.](http://www.public.asu.edu/~rguo12/CIKM18.pdf)|[Python](https://github.com/rguo12/CIKM18-LCVA)|

#### Learning Time Varying/Dependent Causal Effects
|Name|Paper|Code|
|---|---|---|
|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." arXiv preprint arXiv:1902.00450 (2019).](https://arxiv.org/pdf/1902.00450.pdf)|NA|
|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)|

## Connections to Machine Learning
### Recommendation as Causal Inference
|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)|
|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)|
|Deconfounded Recsys|[Wang, Yixin, Dawen Liang, Laurent Charlin, and David M. Blei. "The Deconfounded Recommender: A Causal Inference Approach to Recommendation." arXiv preprint arXiv:1808.06581 (2018).](https://arxiv.org/pdf/1808.06581)|NA|

### Natural Language Processing
|Name|Paper|Code|
|---|---|---|
||[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)|

### Feature Selection
|Name|Paper|Code|
|---|---|---|
|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.](https://www.aclweb.org/anthology/K/K17/K17-1018.pdf)|NA|

### Reinforcement Learning
|Name|Paper|Code|
|---|---|---|
|Casual Bandit|Lee, Sanghack, and Elias Bareinboim. Structural Causal Bandits with Non-manipulable Variables. Technical Report R-40, Purdue AI Lab, Department of Computer Science, Purdue University, 2019.|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|

### Offline Policy Evaluation
|Name|Paper|Code|
|---|---|---|
|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)|

### Invariant Prediction
|Name|Paper|Code|
|---|---|---|
|Deep Global Balancing Regression|Kuang, Kun, Peng Cui, Susan Athey, Ruoxuan Xiong, and Bo Li. "Stable Prediction across Unknown Environments." In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1617-1626. ACM, 2018.|NA|
|A Simple Algorithm for Invariant Prediction| |[Julia](https://github.com/richardkwo/InvariantCausal.jl)|

### Causality and GAN
|Name|Paper|Code|
|---|---|---|
||Odena, Augustus, Jacob Buckman, Catherine Olsson, Tom B. Brown, Christopher Olah, Colin Raffel, and Ian Goodfellow. "Is Generator Conditioning Causally Related to GAN Performance?." arXiv preprint arXiv:1802.08768 (2018).|NA|
|Causal GAN|Kocaoglu, Murat, Christopher Snyder, Alexandros G. Dimakis, and Sriram Vishwanath. "CausalGAN: Learning Causal Implicit Generative Models with Adversarial Training." arXiv preprint arXiv:1709.02023 (2017).|[Python](https://github.com/mkocaoglu/CausalGAN)|



## Learning Causal Relationships
### With i.i.d. Data
|Name|Paper|Code|
|---|---|---|
|[TETRAD toolbox](http://www.phil.cmu.edu/tetrad/about.html)|   |[R](https://github.com/bd2kccd/r-causal)|
|CausalDiscoveryToolbox| |[Python](https://github.com/Diviyan-Kalainathan/CausalDiscoveryToolbox)|
|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| |[R](https://github.com/cran/pcalg)|

#### 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.](http://www.jmlr.org/papers/volume18/17-014/17-014.pdf)|NA|

#### 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)|

#### Conditional Independence Tests (for Constraint-based Algorithms)
|Name|Paper|Code|
|---|---|---|
|RCIT|   |[R](https://github.com/ericstrobl/RCIT)|

#### Causal Discovery Meets 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)|

### Learning Causal Relationships with non-i.i.d. Data

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