awesome-causality-algorithms

An index of algorithms for learning causality with data.

Please cite our survey paper 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}
}

Updates are coming soon!

Learning Causal Effects

Learning Causal Effects with Unconfoundedness Assumption

Name	Paper	Code
Propensity Score Matching		Python
Inverse Probability Reweighting		R
Nonparametric Regression Adjustment		Python
Doubly Robust Estimation		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
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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
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).	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
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
Meta-learners for Estimating Heterogeneous Treatment Effects using Machine Learning	Künzel, Sören 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).	R
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).	R Python
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
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
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.	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.	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.	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
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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.	NA
Adversarial Balancing	Ozery-Flato, Michal, Pierre Thodoroff, and Tal El-Hay. "Adversarial Balancing for Causal Inference." arXiv preprint arXiv:1810.07406 (2018).	NA
DeepMatch	Kallus, Nathan. "Deepmatch: Balancing deep covariate representations for causal inference using adversarial training." arXiv preprint arXiv:1802.05664 (2018).	NA
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Treatment Responder Classification
RespSVM	Kallus, Nathan. "Classifying Treatment Responders Under Causal Effect Monotonicity." arXiv preprint arXiv:1902.05482 (2019)	NA

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.	Python

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).	Python

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).	NA
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
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

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).	Python
	Ranganath, Rajesh, and Adler Perotte. "Multiple causal inference with latent confounding." arXiv preprint arXiv:1805.08273 (2018).	NA

Transfer Learning for Learning Causal Effects

Name	Paper	Code
The Y-learner	Künzel, Sören 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).	NA

Variable Selection/Importance for Learning Causal Effects

Name	Paper	Code
Variable importance through targeted causal inference		R

Learning Causal Effects without the Unconfoundedness Assumption

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.	Python
PDSLasso		STATA

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)	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
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).	NA

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.	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).	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

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

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

Learning Causal Relationships

Learning Causal Relationships with i.i.d. Data

Name	Paper	Code
TETRAD toolbox		R
CausalDiscoveryToolbox		Python
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		R

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

Conditional Independence Tests (for Constraint-based Algorithms)

Name	Paper	Code
RCIT		R

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

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