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awesome-causality-algorithms
An index of algorithms for learning causality with data
https://github.com/rguo12/awesome-causality-algorithms
Last synced: 5 days ago
JSON representation
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Causal Effect Estimation
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With i.i.d Data
- 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 - curve)|
- 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).
- Jean Kaddour, Qi Liu, Yuchen Zhu, Matt J. Kusner, Ricardo Silva. "Causal Effect Inference for Structured Treatments", In NeurIPS 2021.
- Kallus, Nathan. "Classifying Treatment Responders Under Causal Effect Monotonicity." arXiv preprint arXiv:1902.05482 (2019) - ->
- Wang, Yixin, and David M. Blei. "The blessings of multiple causes." arXiv preprint arXiv:1805.06826 (2018). - lab/deconfounder_tutorial)|
- Imai, Kosuke, and Zhichao Jiang. "Discussion of "The Blessings of Multiple Causes" by Wang and Blei."
- 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.
- 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.
- D'Amour, Alexander. "On multi-cause causal inference with unobserved confounding: Counterexamples, impossibility, and alternatives." arXiv preprint arXiv:1902.10286 (2019).
- Ranganath, Rajesh, and Adler Perotte. "Multiple causal inference with latent confounding." arXiv preprint arXiv:1805.08273 (2018).
- Kong, Dehan, Shu Yang, and Linbo Wang. "Multi-cause causal inference with unmeasured confounding and binary outcome." arXiv preprint arXiv:1907.13323 (2019).
- Elizabeth L. Ogburn, Ilya Shpitser, Eric J. Tchetgen Tchetgen "Comment on Blessings of Multiple Causes." arXiv preprint arXiv:1910.05438 (2019) - ->
- Weiss, Sam. Estimating and Visualizing Business Tradeoffs in Uplift Models
- 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). - ->
- Estimating individual treatment effect: generalization bounds and algorithms
- Learning representations for counterfactual inference
- Causal effect inference with deep latent-variable models - Amsterdam/CEVAE)|
- Adapting neural networks for the estimation of treatment effects.
- Representation Learning for Treatment Effect Estimation from Observational Data - Yi/SITE)|
- GANITE: Estimation of Individualized Treatment Effects using Generative Adversarial Nets
- Perfect match: A simple method for learning representations for counterfactual inference with neural networks
- code
- CausalEGM: a general causal inference framework by encoding generative modeling
- 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. - ->
- Alaa, Ahmed M., Michael Weisz, and Mihaela van der Schaar. "Deep counterfactual networks with propensity-dropout." arXiv preprint arXiv:1706.05966 (2017) - ->
- 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.
- Wager, Stefan, and Susan Athey. "Estimation and inference of heterogeneous treatment effects using random forests." JASA (2017). - labs/grf), [code Python](https://github.com/kjung/scikit-learn)|
- S. Powers et al., “Some methods for heterogeneous treatment effect estimation in high-dimensions,” 2017. - labs/grf), [code R](https://github.com/saberpowers/causalLearning)|
- Hill, Jennifer L. "Bayesian nonparametric modeling for causal inference." Journal of Computational and Graphical Statistics 20, no. 1 (2011): 217-240.
- Active Learning for Decision-Making from Imbalanced Observational Data - ->
- Adversarial Balancing-based Representation Learning for Causal Effect Inference with Observational Data - ->
- 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. - ->
- 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. - ->
- 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. - ->
- 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.
- Bang, Heejung, and James M. Robins. "Doubly robust estimation in missing data and causal inference models." Biometrics 61, no. 4 (2005): 962-973.
- Antonelli, Joseph, Matthew Cefalu, Nathan Palmer, and Denis Agniel. "Doubly robust matching estimators for high dimensional confounding adjustment." Biometrics (2016).
- Gruber, Susan, and Mark J. van der Laan. "tmle: An R package for targeted maximum likelihood estimation." (2011). - project.org/web/packages/tmle/index.html)|
- 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.
- 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.
- 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.
- Matlab
- 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. - ->
- Ozery-Flato, Michal, Pierre Thodoroff, and Tal El-Hay. "Adversarial Balancing for Causal Inference." arXiv preprint arXiv:1810.07406 (2018). - ->
- Kallus, Nathan. "Deepmatch: Balancing deep covariate representations for causal inference using adversarial training." arXiv preprint arXiv:1802.05664 (2018). - ->
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- Adversarial Balancing-based Representation Learning for Causal Effect Inference with Observational Data - ->
- 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.
- 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.
- 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.
- Estimating individual treatment effect: generalization bounds and algorithms
- 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.
- 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.
- Jean Kaddour, Qi Liu, Yuchen Zhu, Matt J. Kusner, Ricardo Silva. "Causal Effect Inference for Structured Treatments", In NeurIPS 2021.
- 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.
- 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.
- 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.
- 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.
- 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.
- Perfect match: A simple method for learning representations for counterfactual inference with neural networks
- Alaa, Ahmed M., Michael Weisz, and Mihaela van der Schaar. "Deep counterfactual networks with propensity-dropout." arXiv preprint arXiv:1706.05966 (2017) - ->
- 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.
- 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.
- Ranganath, Rajesh, and Adler Perotte. "Multiple causal inference with latent confounding." arXiv preprint arXiv:1805.08273 (2018).
- Ozery-Flato, Michal, Pierre Thodoroff, and Tal El-Hay. "Adversarial Balancing for Causal Inference." arXiv preprint arXiv:1810.07406 (2018). - ->
- Kallus, Nathan. "Deepmatch: Balancing deep covariate representations for causal inference using adversarial training." arXiv preprint arXiv:1802.05664 (2018). - ->
- 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.
- 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.
- 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.
- 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).
- Kallus, Nathan. "Classifying Treatment Responders Under Causal Effect Monotonicity." arXiv preprint arXiv:1902.05482 (2019) - ->
- Kong, Dehan, Shu Yang, and Linbo Wang. "Multi-cause causal inference with unmeasured confounding and binary outcome." arXiv preprint arXiv:1907.13323 (2019).
- 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). - ->
- 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.
- S. Powers et al., “Some methods for heterogeneous treatment effect estimation in high-dimensions,” 2017. - labs/grf), [code R](https://github.com/saberpowers/causalLearning)|
- 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.
- 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.
- 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.
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Non-i.i.d Data
- Abadie, Alberto. "Using synthetic controls: Feasibility, data requirements, and methodological aspects." Journal of Economic Literature 59.2 (2021): 391-425. - project.org/web/packages/Synth/Synth.pdf)|
- Arkhangelsky, Dmitry, et al. Synthetic difference in differences. No. w25532. National Bureau of Economic Research, 2019. - inference/synthdid)<br>[Python](https://github.com/MasaAsami/pysynthdid)|
- 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
- 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. - Series-Deconfounder)|
- Lim, Bryan. "Forecasting Treatment Responses Over Time Using Recurrent Marginal Structural Networks." In Advances in Neural Information Processing Systems, pp. 7494-7504. 2018.
- 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.
- Melnychuk, Valentyn, Dennis Frauen, and Stefan Feuerriegel. "Causal Transformer for Estimating Counterfactual Outcomes." arXiv preprint arXiv:2204.07258 (2022).
- Guo, Ruocheng, Jundong Li, and Huan Liu. "Learning Individual Causal Effects from Networked Observational Data." WSDM 2020. - deconfounder-wsdm20)|
- Ma, Yunpu, Yuyi Wang, and Volker Tresp. "Causal Inference under Networked Interference." arXiv preprint arXiv:2002.08506 (2020). - ->
- Veitch, Victor, Yixin Wang, and David M. Blei. "Using embeddings to correct for unobserved confounding." arXiv preprint arXiv:1902.04114 (2019). - network-embeddings)|
- Rakesh, Vineeth, Ruocheng Guo, Raha Moraffah, Nitin Agarwal, and Huan Liu. "Linked Causal Variational Autoencoder for Inferring Paired Spillover Effects." CIKM 2018. - LCVA)|
- Li, Wenrui, Daniel L. Sussman, and Eric D. Kolaczyk. "Causal Inference under Network Interference with Noise." arXiv preprint arXiv:2105.04518 (2021).
- Veitch, Victor, Yixin Wang, and David M. Blei. "Using embeddings to correct for unobserved confounding." arXiv preprint arXiv:1902.04114 (2019). - network-embeddings)|
- 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. - Series-Deconfounder)|
- Ma, Yunpu, Yuyi Wang, and Volker Tresp. "Causal Inference under Networked Interference." arXiv preprint arXiv:2002.08506 (2020). - ->
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Rootcause Analysis
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Causal Machine Learning
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Surveys
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OoD Generalization
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Recommendation Systems
- 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.
- Schnabel, Tobias, Adith Swaminathan, Ashudeep Singh, Navin Chandak, and Thorsten Joachims. "Recommendations as treatments: Debiasing learning and evaluation." arXiv preprint arXiv:1602.05352 (2016).
- 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. - ->
- 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**) - research/CausE)|
- Saito, Yuta, and Masahiro Nomura. "Towards Resolving Propensity Contradiction in Offline Recommender Learning." In IJCAI 2022 - adversarial-mf)|
- 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). - fib-lab/DICE)|
- 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. - implicit-rec-real)|
- 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). - recsys-public)|
- 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. - offline-recommender-evaluation)|
- 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**) - research/CausE)|
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Learning to Rank
- 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. - ->
- 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. - ->
- 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. - ->
- 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.
- 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).
- 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.
- 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 - Learning-to-Rank-with-Unbiased-Propensity-Estimation)|
- 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. - ->
- 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.
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Off-line Policy Evaluation/Optimization (for Contextual Bandit or RL)
- Andrew Bennett, Nathan Kallus. "Policy Evaluation with Latent Confounders via Optimal Balance"
- Joachims, Thorsten, Adith Swaminathan, and Maarten de Rijke. "Deep learning with logged bandit feedback." (2018).
- Swaminathan, Adith, and Thorsten Joachims. "Counterfactual risk minimization: Learning from logged bandit feedback." In International Conference on Machine Learning, pp. 814-823. 2015.
- Swaminathan, Adith, and Thorsten Joachims. "The self-normalized estimator for counterfactual learning." In Advances in Neural Information Processing Systems, pp. 3231-3239. 2015.
- 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). - ->
- Kallus, Nathan. "Balanced policy evaluation and learning." In Advances in Neural Information Processing Systems, pp. 8895-8906. 2018. - ->
- Kallus, Nathan, and Angela Zhou. "Confounding-robust policy improvement." In Advances in Neural Information Processing Systems, pp. 9269-9279. 2018. - ->
- Zhou, Zhengyuan, Susan Athey, and Stefan Wager. "Offline multi-action policy learning: Generalization and optimization." arXiv preprint arXiv:1810.04778 (2018). - ->
- Athey, Susan, and Stefan Wager. "Efficient policy learning." arXiv preprint arXiv:1702.02896 (2017). - ->
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Natural Language Processing
- 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. - causal)|
- 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).
- 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.
- Pryzant, Reid, Kelly Shen, Dan Jurafsky, and Stefan Wagner. "Deconfounded lexicon induction for interpretable social science." NAACL 2018.
- Veitch, Victor, Dhanya Sridhar, and David M. Blei. "Using Text Embeddings for Causal Inference." arXiv preprint arXiv:1905.12741 (2019). - lab/causal-text-embeddings)|
- Michael J. Paul. Feature selection as causal inference: experiments with text classification. Conference on Computational Natural Language Learning (CoNLL), Vancouver, Canada. August 2017. - ->
- 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. - ->
- Veitch, Victor, Dhanya Sridhar, and David M. Blei. "Using Text Embeddings for Causal Inference." arXiv preprint arXiv:1905.12741 (2019). - lab/causal-text-embeddings)|
- 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).
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Counterfactual Explanations
- Mothilal, Ramaravind Kommiya, Amit Sharma, and Chenhao Tan. "Explaining machine learning classifiers through diverse counterfactual explanations." arXiv preprint arXiv:1905.07697 (2019).
- 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.
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Counterfactual Fairness
- Kusner, Matt J., Joshua Loftus, Chris Russell, and Ricardo Silva. "Counterfactual fairness." In Advances in Neural Information Processing Systems, pp. 4066-4076. 2017. - 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). - specific-Counterfactual-Fairness)|
- Chiappa, Silvia. "Path-specific counterfactual fairness." In Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 7801-7808. 2019. - source/path-specific-counterfactual-fairness-in-jax)|
- 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. - ->
- 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. - fairness)|
- Chiappa, Silvia. "Path-specific counterfactual fairness." In Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 7801-7808. 2019. - source/path-specific-counterfactual-fairness-in-jax)|
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Reinforcement Learning
- Lu, Chaochao, Bernhard Schölkopf, and José Miguel Hernández-Lobato. "Deconfounding reinforcement learning in observational settings." arXiv preprint arXiv:1812.10576 (2018).
- Vansteelandt, Stijn, and Marshall Joffe. "Structural nested models and G-estimation: the partially realized promise." Statistical Science 29, no. 4 (2014): 707-731.
- Lu, Chaochao, Bernhard Schölkopf, and José Miguel Hernández-Lobato. "Deconfounding reinforcement learning in observational settings." arXiv preprint arXiv:1812.10576 (2018).
- Vansteelandt, Stijn, and Marshall Joffe. "Structural nested models and G-estimation: the partially realized promise." Statistical Science 29, no. 4 (2014): 707-731.
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Multi-Armed Bandit/Causal Bandit
- 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.
- 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).
- 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.
- Sawant, Neela, Chitti Babu Namballa, Narayanan Sadagopan, and Houssam Nassif. "Contextual Multi-Armed Bandits for Causal Marketing." arXiv preprint arXiv:1810.01859 (2018).
- Sawant, Neela, Chitti Babu Namballa, Narayanan Sadagopan, and Houssam Nassif. "Contextual Multi-Armed Bandits for Causal Marketing." arXiv preprint arXiv:1810.01859 (2018).
- 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).
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Causal Discovery
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for i.i.d. Data
- DAGs with NO TEARS: Continuous optimization for structure learning
- DAG-GNN: DAG Structure Learning with Graph Neural Networks - GNN)|
- Learning Sparse Nonparametric DAGs
- Amortized Inference for Causal Structure Learning
- Learning instrumental variables with structural and non-gaussianity assumptions
- R
- Java
- 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. - data-lab-umbc/ensemble_causality_learning)|
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with Temporal Data
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Toolboxes
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Comprehensive
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Treatment Effect Estimation / Uplift Modeling
- Tutorial on Causal Inference and Counterfactual Reasoning
- Causalml: Python package for causal machine learning
- Underlying thesis
- Documentation
- Documentation - 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. |
- Causalml: Python package for causal machine learning
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Causal Discovery
- Bench Press
- causal-learn - phil/causal-learn)|Causal Discovery for Python. A translation and extension of TETRAD.|
- TETRAD R/Java - 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|
- Causal Discovery Toolbox: Uncover causal relationships in Python - Kalainathan/CausalDiscoveryToolbox)||
- Causal Discovery Toolbox: Uncover causal relationships in Python - Kalainathan/CausalDiscoveryToolbox)||
-
Rootcause Analysis
- Chaos Genius - genius/chaos_genius/)|ML powered analytics engine for outlier/anomaly detection and root cause analysis.|
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Programming Languages
Sub Categories
With i.i.d Data
83
Non-i.i.d Data
15
Recommendation Systems
10
Learning to Rank
9
Off-line Policy Evaluation/Optimization (for Contextual Bandit or RL)
9
Natural Language Processing
9
for i.i.d. Data
8
Multi-Armed Bandit/Causal Bandit
6
Treatment Effect Estimation / Uplift Modeling
6
Counterfactual Fairness
6
Causal Discovery
5
Reinforcement Learning
4
Counterfactual Explanations
2
Comprehensive
2
Rootcause Analysis
2
Surveys
1
OoD Generalization
1
with Temporal Data
1