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awesome-causality-algorithms Awesome

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.

  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)},
  publisher={ACM New York, NY, USA}

Table of Contents



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
Classic Methods
Name Paper Code
Propensity Score Matching Rosenbaum, Paul R., and Donald B. Rubin. "The central role of the propensity score in observational studies for causal effects." Biometrika 70, no. 1 (1983): 41-55. Python
Tree based Methods
Name Paper Code
Causal Forest Wager, Stefan, and Susan Athey. "Estimation and inference of heterogeneous treatment effects using random forests." JASA (2017). code R, code Python
Causal MARS, Causal Boosting, Pollinated Transformed Outcome Forests S. Powers et al., “Some methods for heterogeneous treatment effect estimation in high-dimensions,” 2017. code R, code R
Bayesian Additive Regression Trees (BART) Hill, Jennifer L. "Bayesian nonparametric modeling for causal inference." Journal of Computational and Graphical Statistics 20, no. 1 (2011): 217-240. Python
Meta Learner
Name Paper Code
X-learner Künzel, Sören R., Jasjeet S. Sekhon, Peter J. Bickel, and Bin Yu. "Metalearners for estimating heterogeneous treatment effects using machine learning." Proceedings of the National Academy of Sciences 116, no. 10 (2019): 4156-4165. code R, code R

Average Treatment Effect (including ATT and ATC)

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

Instrumental Variable (IV)

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

Does-Response Curve (Continuous Treatment)

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

Vectorized Treatments

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

Multiple Causes

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

Multiple Outcomes

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

Non-i.i.d Data

Panel Data / Time Series

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

Network Data (with or without Interference)

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

Causal Machine Learning


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

OoD Generalization

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

Graph OoD Generalization

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

Recommendation Systems

Inverse Propensity Scoring / Doubly Robust

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

Hidden Confounding

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

Domain Adaptation

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


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

Learning to Rank

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

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

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

Natural Language Processing

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

Counterfactual Explanations

Paper Code
Mothilal, Ramaravind Kommiya, Amit Sharma, and Chenhao Tan. "Explaining machine learning classifiers through diverse counterfactual explanations." arXiv preprint arXiv:1905.07697 (2019). Python
Russell, Chris. "Efficient search for diverse coherent explanations." In Proceedings of the Conference on Fairness, Accountability, and Transparency, pp. 20-28. 2019. Python
Wachter, Sandra, Brent Mittelstadt, and Chris Russell. "Counterfactual explanations without opening the black box: Automated decisions and the GDPR." Harv. JL & Tech. 31 (2017): 841.

Counterfactual Fairness

Paper Code
Kusner, Matt J., Joshua Loftus, Chris Russell, and Ricardo Silva. "Counterfactual fairness." In Advances in Neural Information Processing Systems, pp. 4066-4076. 2017. Python
Wu, Yongkai, Lu Zhang, Xintao Wu, and Hanghang Tong. "Pc-fairness: A unified framework for measuring causality-based fairness." Advances in Neural Information Processing Systems 32 (2019). code
Chiappa, Silvia. "Path-specific counterfactual fairness." In Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 7801-7808. 2019. code
Garg, Sahaj, Vincent Perot, Nicole Limtiaco, Ankur Taly, Ed H. Chi, and Alex Beutel. "Counterfactual fairness in text classification through robustness." In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society, pp. 219-226. 2019. code

Reinforcement Learning

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

Multi-Armed Bandit/Causal Bandit

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

Causal Discovery

for i.i.d. Data

Classic Methods

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

Continuous Optimization

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

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