{"id":13503581,"url":"https://github.com/jvpoulos/causal-ml","last_synced_at":"2025-03-29T18:31:22.024Z","repository":{"id":39850142,"uuid":"214910073","full_name":"jvpoulos/causal-ml","owner":"jvpoulos","description":"Must-read papers and resources related to causal inference and machine (deep) learning","archived":false,"fork":false,"pushed_at":"2022-11-23T14:35:46.000Z","size":83,"stargazers_count":670,"open_issues_count":0,"forks_count":125,"subscribers_count":28,"default_branch":"master","last_synced_at":"2024-11-01T00:31:51.496Z","etag":null,"topics":["causal-discovery","causal-inference","causal-learning","causal-models","counterfactual","counterfactual-learning","deep-learning","estimating-treatment-effects","heterogeneous-treatment-effects","machine-learning","paper-list","randomized-controlled-trials","representation-learning","treatment-effects"],"latest_commit_sha":null,"homepage":"","language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/jvpoulos.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2019-10-13T23:39:10.000Z","updated_at":"2024-10-23T00:10:24.000Z","dependencies_parsed_at":"2023-01-22T10:00:45.316Z","dependency_job_id":null,"html_url":"https://github.com/jvpoulos/causal-ml","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jvpoulos%2Fcausal-ml","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jvpoulos%2Fcausal-ml/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jvpoulos%2Fcausal-ml/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jvpoulos%2Fcausal-ml/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/jvpoulos","download_url":"https://codeload.github.com/jvpoulos/causal-ml/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":246226991,"owners_count":20743866,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["causal-discovery","causal-inference","causal-learning","causal-models","counterfactual","counterfactual-learning","deep-learning","estimating-treatment-effects","heterogeneous-treatment-effects","machine-learning","paper-list","randomized-controlled-trials","representation-learning","treatment-effects"],"created_at":"2024-07-31T23:00:40.829Z","updated_at":"2025-03-29T18:31:21.746Z","avatar_url":"https://github.com/jvpoulos.png","language":null,"funding_links":[],"categories":["Topics","Related Repos","🚀 GitHub Repositories"],"sub_categories":["Research Paper","🌟 **Real-World Magic**"],"readme":"# Must-read recent papers and resources on {Causal}∩{ML}\n\nContributions are welcome. Inspired by [GNNpapers](https://github.com/thunlp/GNNPapers).\n\n## [Content](#content)\n\n\u003ctable\u003e\n\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003ca href=\"#survey-papers\"\u003e1. Surveys\u003c/a\u003e\u003c/td\u003e\u003c/tr\u003e \n\u003ctr\u003e\n\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003ca href=\"#individual-treatment-effects\"\u003e2. Individual treatment effects\u003c/a\u003e\u003c/td\u003e\u003c/tr\u003e\n    \u003ctd\u003e\u0026emsp;\u003ca href=\"#heterogeneous-treatment-effects\"\u003e2.1. Heterogeneous treatment effects\u003c/a\u003e\u003c/td\u003e\n    \u003ctd\u003e\u0026emsp;\u003ca href=\"#static-data\"\u003e2.2. Static data\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e \n\u003ctr\u003e\n    \u003ctd\u003e\u0026emsp;\u003ca href=\"#temporal-data\"\u003e2.3. Temporal data\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e \n\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003ca href=\"#representation-learning\"\u003e3. Representation learning\u003c/a\u003e\u003c/td\u003e\u003c/tr\u003e\n\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003ca href=\"#semiparametric-inference\"\u003e4. Semiparametric / double robust inference\u003c/a\u003e\u003c/td\u003e\u003c/tr\u003e\n\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003ca href=\"#policy-learning\"\u003e5. Policy learning / causal discovery\u003c/a\u003e\u003c/td\u003e\u003c/tr\u003e\n\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003ca href=\"#causal-recommendation\"\u003e6. Causal recommendation\u003c/a\u003e\u003c/td\u003e\u003c/tr\u003e\n\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003ca href=\"#causal-reinforcement-learning\"\u003e7. Causal reinforcement learning\u003c/a\u003e\u003c/td\u003e\u003c/tr\u003e\n\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003ca href=\"#causal-reinforcement-learning\"\u003e8. Feature selection in causal inference\u003c/a\u003e\u003c/td\u003e\u003c/tr\u003e\n\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003ca href=\"#applications\"\u003e9. Applications\u003c/a\u003e\u003c/td\u003e\u003c/tr\u003e\n    \u003ctd\u003e\u0026emsp;\u003ca href=\"#social-sciences\"\u003e9.1. Social Sciences\u003c/a\u003e\u003c/td\u003e\n    \u003ctd\u003e\u0026ensp;\u003ca href=\"#text\"\u003e9.2. Text\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e \n\u003ctr\u003e\n    \u003ctd\u003e\u0026ensp;\u003ca href=\"#health\"\u003e9.3. Health\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e \n\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003ca href=\"#resources\"\u003e10. Resources\u003c/a\u003e\u003c/td\u003e\u003c/tr\u003e \n    \u003ctd\u003e\u0026emsp;\u003ca href=\"#workshops\"\u003e10.1. Workshops\u003c/a\u003e\u003c/td\u003e\n    \u003ctd\u003e\u0026emsp;\u003ca href=\"#proceedings\"\u003e10.2. Proceedings\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e \n\u003ctr\u003e\n    \u003ctd\u003e\u0026ensp;\u003ca href=\"#code-libraries\"\u003e10.3. Code libraries\u003c/a\u003e\u003c/td\u003e\n    \u003ctd\u003e\u0026emsp;\u003ca href=\"#benchmark-datasets\"\u003e10.4. Benchmark datasets\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e \n\u003ctr\u003e\n    \u003ctd\u003e\u0026emsp;\u003ca href=\"#courses\"\u003e10.5. Courses\u003c/a\u003e\u003c/td\u003e\n    \u003ctd\u003e\u0026emsp;\u003ca href=\"#industry\"\u003e10.6. Industry\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e \n\u003ctr\u003e\n    \u003ctd\u003e\u0026emsp;\u003ca href=\"#groups\"\u003e10.7. Groups\u003c/a\u003e\u003c/td\u003e\n    \u003ctd\u003e\u0026emsp;\u003ca href=\"#lists\"\u003e10.8. Lists\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e \n\u003ctr\u003e\n    \u003ctd\u003e\u0026emsp;\u003ca href=\"#books\"\u003e10.9. Books\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e \n\u003c/table\u003e\n\n## [Survey papers](#content)\n\n1. **Causal Machine Learning: A Survey and Open Problems**, 2022. [paper](https://arxiv.org/abs/2206.15475)\n\n    Jean Kaddour, Aengus Lynch, Qi Liu, Matt J. Kusner, Ricardo Silva.\n\t\n1. **A Unified Survey of Heterogeneous Treatment Effect Estimation and Uplift Modeling**, *ACM Computing Surveys*, 2022. [paper](https://dl.acm.org/doi/abs/10.1145/3466818)\n\n    Weijia Zhang, Jiuyong Li, Lin Liu.\n\n1. **Toward Causal Representation Learning**, *IEEE*, 2021. [paper](https://ieeexplore.ieee.org/abstract/document/9363924)\n    \n    Bernhard Schölkopf, Francesco Locatello, Stefan Bauer, Nan Rosemary Ke, Nal Kalchbrenner, Anirudh Goyal, Yoshua Bengio.\n\n1. **A Survey of Learning Causality with Data: Problems and Methods**, *ACM*, 2020. [paper](https://arxiv.org/abs/1809.09337)\n    \n    Ruocheng Guo, Lu Cheng, Jundong Li, P. Richard Hahn, Huan Liu.\n\n1. **Machine learning and causal inference for policy evaluation**, *KDD*, 2015. [paper](https://dl.acm.org/citation.cfm?id=2785466)\n    \n    Susan Athey.\n\n## [Individual treatment effects](#content) \n\n### [Heterogeneous treatment effects](#content)  \n\n1. **Can Transformers be Strong Treatment Effect Estimators?**, *arxiv*, 2022. [paper](https://arxiv.org/abs/2202.01336) [code](https://github.com/hlzhang109/TransTEE)\n\n    Yi-Fan Zhang, Hanlin Zhang, Zachary C. Lipton, Li Erran Li, Eric P. Xing.\n\n1. **Nonparametric Estimation of Heterogeneous Treatment Effects: From Theory to Learning Algorithms**, *AISTATS*, 2021. [paper](https://proceedings.mlr.press/v130/curth21a.html)\n    \n    Alicia Curth, Mihaela van der Schaar.\n\n1. **Causal Effect Inference for Structured Treatments**, *NeurIPS*, 2021. [paper](https://arxiv.org/abs/2106.01939) [code](https://github.com/JeanKaddour/SIN)\n    \n    Jean Kaddour, Yuchen Zhu, Qi Liu, Matt J. Kusner, Ricardo Silva.\n\t\n1. **Treatment Effect Estimation with Disentangled Latent Factors**, *AAAI*, 2021. [paper](https://ojs.aaai.org/index.php/AAAI/article/view/17304) [code](https://github.com/WeijiaZhang24/TEDVAE)\n    \n    Weijia Zhang, Lin Liu, Jiuyong Li.\n\n1. **Generic Machine Learning Inference on Heterogenous Treatment Effects in Randomized Experiments**, *arXiv*, 2020. [paper](https://arxiv.org/abs/1712.04802)\n\n    Victor Chernozhukov, Mert Demirer, Esther Duflo, Iván Fernández-Val.\n\n1. **Quasi-Oracle Estimation of Heterogeneous Treatment Effects**, *arXiv*, 2019. [paper](https://arxiv.org/abs/1712.04912)\n\n    Xinkun Nie, Stefan Wager.\n\n1. **Generalized Random Forests**, *Annals of Statistics*, 2019. [paper](https://arxiv.org/abs/1610.01271)\n\n    Susan Athey, Julie Tibshirani, Stefan Wager.\n\n1. **Machine Learning Estimation of Heterogeneous Treatment Effects with Instruments**, *NeurIPS*, 2019. [paper](https://arxiv.org/abs/1905.10176)\n    \n    Vasilis Syrgkanis, Victor Lei, Miruna Oprescu, Maggie Hei, Keith Battocchi, Greg Lewis.\n\n1. **Orthogonal Random Forest for Causal Inference**, *PMLR*, 2019. [paper](http://proceedings.mlr.press/v97/oprescu19a.html)\n\n    Miruna Oprescu, Vasilis Syrgkanis, Zhiwei Steven Wu.\n\n1. **Meta-learners for Estimating Heterogeneous Treatment Effects using Machine Learning**, *PNAS*, 2019. [paper](https://arxiv.org/abs/1706.03461)\n\n    Sören R. Künzel, Jasjeet S. Sekhon, Peter J. Bickel, Bin Yu.\n\n1. **Machine Learning Analysis of Heterogeneity in the Effect of Student Mindset Interventions**, *Observational Studies*, 2019. [paper](https://arxiv.org/abs/1811.05975)\n    \n    Fredrik D. Johansson.\n\n1. **Estimation and Inference of Heterogeneous Treatment Effects using Random Forests**, *JASA*, 2018. [paper](https://amstat.tandfonline.com/doi/full/10.1080/01621459.2017.1319839#.XaPLBeZKhhE)\n    \n    Stefan Wager, Susan Athey.\n\n1. **Limits of Estimating Heterogeneous Treatment Effects: Guidelines for Practical Algorithm Design**, *PMLR*, 2018. [paper](http://proceedings.mlr.press/v80/alaa18a.html)\n    \n    Ahmed Alaa, Mihaela Schaar.\n\n1. **Transfer Learning for Estimating Causal Effects using Neural Networks**, *arXiv*, 2018. [paper](https://arxiv.org/abs/1808.07804)\n\n    Sören R. Künzel, Bradly C. Stadie, Nikita Vemuri, Varsha Ramakrishnan, Jasjeet S. Sekhon, Pieter Abbeel.\n\n1. **Recursive partitioning for heterogeneous causal effects**, *PNAS*, 2016. [paper](https://www.pnas.org/content/113/27/7353)\n    \n    Susan Athey, Guido Imbens.\n\n1. **Machine Learning Methods for Estimating Heterogeneous Causal Effects**, *ArXiv*, 2015. [paper](https://arxiv.org/abs/1504.01132v1)\n\n    Susan Athey, Guido W. Imbens.\n\n\n### [Static data](#content) \n\n1. **VCNet and Functional Targeted Regularization For Learning Causal Effects of Continuous Treatments**, *ICLR*, 2021. [paper](https://arxiv.org/abs/2103.07861)  [code](https://github.com/lushleaf/varying-coefficient-net-with-functional-tr)\n\n    Lizhen Nie, Mao Ye, Qiang Liu, Dan Nicolae.\n\n1. **Learning Counterfactual Representations for Estimating Individual Dose-Response Curves**, *AAAI*, 2020. [paper](https://arxiv.org/abs/1902.00981) [code](https://github.com/d909b/drnet)\n\n    Patrick Schwab, Lorenz Linhardt, Stefan Bauer, Joachim M. Buhmann, Walter Karlen.\n\n1. **Estimating the Effects of Continuous-valued Interventions using Generative Adversarial Networks**, *NeurIPS*, 2020. [paper](https://arxiv.org/abs/2002.12326) [code](https://github.com/ioanabica/SCIGAN)\n\n    Ioana Bica, James Jordon, Mihaela van der Schaar.\n\n1. **Learning Individual Causal Effects from Networked Observational Data**, *WSDM*, 2020. [paper](https://arxiv.org/abs/1906.03485) [code](https://github.com/rguo12/network-deconfounder-wsdm20)\n\n    Ruocheng Guo, Jundong Li, Huan Liu.\n\n1. **Learning Overlapping Representations for the Estimation of Individualized Treatment Effects**, *AISTATS*, 2020. [paper](https://arxiv.org/abs/2001.04754)\n\n    Yao Zhang, Alexis Bellot, Mihaela van der Schaar.\n\n1. **Adapting Neural Networks for the Estimation of Treatment Effects**, *arXiv*, 2019. [paper](https://arxiv.org/abs/1906.02120) [code](http://github.com/claudiashi57/dragonnet)\n    \n    Claudia Shi, David M. Blei, Victor Veitch.\n\n1. **Program Evaluation and Causal Inference with High-Dimensional Data**, *arXiv*, 2018. [paper](https://arxiv.org/abs/1311.2645)\n    \n    Alexandre Belloni, Victor Chernozhukov, Ivan Fernández-Val, Christian Hansen.    \n\n1. **GANITE: Estimation of Individualized Treatment Effects using Generative Adversarial Nets**, *ICLR*, 2018. [paper](https://openreview.net/pdf?id=ByKWUeWA-) [code](https://github.com/jsyoon0823/GANITE)\n    \n    Jinsung Yoon, James Jordon, Mihaela van der Schaar.\n\n1. **Estimation of Individual Treatment Effect in Latent Confounder Models via Adversarial Learning**, *arXiv*, 2018. [paper](https://arxiv.org/abs/1811.08943)\n    \n    Changhee Lee, Nicholas Mastronarde, Mihaela van der Schaar.\n\n1. **Deep IV: A Flexible Approach for Counterfactual Prediction**, *PMLR*, 2017. [paper](http://proceedings.mlr.press/v70/hartford17a.html)\n    \n    Uri Shalit, Fredrik D. Johansson, David Sontag.\n\n1. **Causal Effect Inference with Deep Latent-Variable Models**, *arXiv*, 2017. [paper](https://arxiv.org/abs/1705.08821) [code](https://github.com/AMLab-Amsterdam/CEVAE)\n    \n    Christos Louizos, Uri Shalit, Joris Mooij, David Sontag, Richard Zemel, Max Welling.\n\n1. **Estimating individual treatment effect: generalization bounds and algorithms**, *PMLR*, 2017. [paper](http://proceedings.mlr.press/v70/shalit17a.html) [code](https://github.com/clinicalml/cfrnet)\n    \n    Uri Shalit, Fredrik D. Johansson, David Sontag.\n\n### [Temporal data](#content) \n\n1. **Time Series Deconfounder: Estimating Treatment Effects over Time in the Presence of Hidden Confounders**, *ICML*, 2020. [paper](https://arxiv.org/abs/1902.00450) [code](https://github.com/ioanabica/Time-Series-Deconfounder)\n\n    Ioana Bica, Ahmed M. Alaa, Mihaela van der Schaar.\n\n1. **Estimating Counterfactual Treatment Outcomes over Time through Adversarially Balanced Representations**, *ICLR*, 2020. [paper](https://openreview.net/pdf?id=BJg866NFvB) [code](https://github.com/ioanabica/Counterfactual-Recurrent-Network)\n\n    Ioana Bica, Ahmed M. Alaa, James Jordon, Mihaela van der Schaar.\n\n1. **Generative Learning of Counterfactual for Synthetic Control Applications in Econometrics**, *arXiv*, 2019. [paper](https://arxiv.org/abs/1910.07178)\n    \n    Chirag Modi, Uros Seljak.\n\n1. **Robust Synthetic Control**, *JMLR*, 2019. [paper](http://www.jmlr.org/papers/volume19/17-777.pdf)\n    \n    Muhammad Amjad, Devavrat Shah, Dennis Shen.\n\n1. **ArCo: An artificial counterfactual approach for high-dimensional panel time-series data**, *Journal of Econometrics*, 2018. [paper](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2823687)\n    \n    Carlos Carvalho, Ricardo Masini, Marcelo C. Medeiros.\n\n1. **Forecasting Treatment Responses Over Time Using Recurrent Marginal Structural Networks**, *NIPS*, 2018. [paper](https://papers.nips.cc/paper/7977-forecasting-treatment-responses-over-time-using-recurrent-marginal-structural-networks) [code](https://github.com/sjblim/rmsn_nips_2018)\n    \n    Sonali Parbhoo, Stefan Bauer, Patrick Schwab.\n\n## [Representation learning](#content)   \n\n1. **Deep Structural Causal Models for Tractable Counterfactual Inference**, *NeurIPS*, 2020. [paper](https://arxiv.org/abs/2006.06485) [code](https://github.com/biomedia-mira/deepscm)\n\n    Nick Pawlowski, Daniel C. Castro, Ben Glocker.\n\n1. **NCoRE: Neural Counterfactual Representation Learning for Combinations of Treatments**, *arXiv*, 2021. [paper](https://arxiv.org/abs/2103.11175)\n    \n    Sonali Parbhoo, Stefan Bauer, Patrick Schwab.\n\n1. **Perfect Match: A Simple Method for Learning Representations For Counterfactual Inference With Neural Networks**, *arXiv*, 2019. [paper](https://arxiv.org/abs/1810.00656) [code](https://github.com/d909b/perfect_match)\n    \n    Patrick Schwab, Lorenz Linhardt, Walter Karlen.\n\n1. **Representation Learning for Treatment Effect Estimation from Observational Data**, *NeurIPS*, 2019. [paper](https://papers.nips.cc/paper/7529-representation-learning-for-treatment-effect-estimation-from-observational-data.pdf) \n    \n    Liuyi Yao et al.\n\n1. **Invariant Models for Causal Transfer Learning**, *JMLR*, 2018. [paper](http://jmlr.org/papers/v19/16-432.html) \n    \n    Mateo Rojas-Carulla, Bernhard Schölkopf, Richard Turner, Jonas Peters.\n\n1. **Learning Representations for Counterfactual Inference**, *arXiv*, 2018. [paper](https://arxiv.org/abs/1605.03661) [code](https://github.com/clinicalml/cfrnet)\n    \n    Fredrik D. Johansson, Uri Shalit, David Sontag.\n\n## [Semiparametric / double robust inference](#content)  \n\n1. **Sparsity Double Robust Inference of Average Treatment Effects**, *arXiv*, 2019. [paper](https://arxiv.org/abs/1905.00744)\n    \n    Jelena Bradic, Stefan Wager, Yinchu Zhu.\n\n1. **Deep Neural Networks for Estimation and Inference**, *arXiv*, 2019. [paper](https://arxiv.org/abs/1809.09953)\n    \n    Max H. Farrell, Tengyuan Liang, Sanjog Misra.\n\n1. **Deep Counterfactual Networks with Propensity-Dropout**, *arXiv*, 2017. [paper](https://arxiv.org/abs/1706.05966)\n    \n    Ahmed M. Alaa, Michael Weisz, Mihaela van der Schaar.\n\n1. **Double/Debiased Machine Learning for Treatment and Causal Parameters**, *arXiv*, 2017. [paper](https://arxiv.org/abs/1608.00060)\n    \n    Victor Chernozhukov, Denis Chetverikov, Mert Demirer, Esther Duflo, Christian Hansen, Whitney Newey, James Robins.\n\n1. **Doubly Robust Policy Evaluation and Optimization**, *Statistical Science*, 2014. [paper](https://arxiv.org/abs/1503.02834)\n    \n    Miroslav Dudík, Dumitru Erhan, John Langford, Lihong Li.\n\n## [Policy learning / causal discovery](#content)  \n\n1. **Differentiable Causal Discovery Under Unmeasured Confounding**, *arXiv*, 2021. [paper](https://arxiv.org/abs/2010.06978)\n    \n    Rohit Bhattacharya, Tushar Nagarajan, Daniel Malinsky, Ilya Shpitser.\n\n1. **Causal Discovery with Attention-Based Convolutional Neural Networks**, *Machine Learning and Knowledge Extraction*, 2019. [paper](https://www.mdpi.com/2504-4990/1/1/19) [code](https://github.com/M-Nauta/TCDF)\n    \n    Meike Nauta, Doina Bucur, Christin Seifert.\n\n1. **A Meta-Transfer Objective for Learning to Disentangle Causal Mechanisms**, *arXiv*, 2019. [paper](https://arxiv.org/abs/1901.10912)\n    \n    Yoshua Bengio, Tristan Deleu, Nasim Rahaman, Rosemary Ke, Sébastien Lachapelle, Olexa Bilaniuk, Anirudh Goyal, Christopher Pal.\n\n1. **Causal Discovery with Reinforcement Learning**, *arXiv*, 2019. [paper](https://arxiv.org/abs/1906.04477)\n    \n    Shengyu Zhu, Zhitang Chen.\n\n1. **CausalGAN: Learning Causal Implicit Generative Models with Adversarial Training**, *arXiv*, 2019. [paper](https://arxiv.org/abs/1709.02023)\n    \n    Murat Kocaoglu, Christopher Snyder, Alexandros G. Dimakis, Sriram Vishwanath.\n\n1. **Learning When-to-Treat Policies**, *arXiv*, 2019. [paper](https://arxiv.org/abs/1905.09751)\n    \n    Xinkun Nie, Emma Brunskill, Stefan Wager.\n\n1. **Learning Neural Causal Models from Unknown Interventions**, *arXiv*, 2019. [paper](https://arxiv.org/abs/1910.01075) [code](https://github.com/nke001/causal_learning_unknown_interventions)\n    \n    Nan Rosemary Ke, Olexa Bilaniuk, Anirudh Goyal, Stefan Bauer, Hugo Larochelle, Chris Pal, Yoshua Bengio.\n\n1. **Counterfactual Policy Optimization Using Domain-Adversarial Neural Networks**, *ICML*, 2018. [paper](http://medianetlab.ee.ucla.edu/papers/cf_treat_v5)\n    \n    Onur Atan, William R. Zame, Mihaela van der Schaar.\n\n1. **Causal Bandits: Learning Good Interventions via Causal Inference**, *NIPS*, 2016. [paper](https://papers.nips.cc/paper/6195-causal-bandits-learning-good-interventions-via-causal-inference)\n    \n    Finnian Lattimore, Tor Lattimore, Mark D. Reid.\n\n1. **Counterfactual Risk Minimization: Learning from Logged Bandit Feedback**, *arXiv*, 2015. [paper](https://arxiv.org/abs/1502.02362)\n    \n    Adith Swaminathan, Thorsten Joachims.\n\n## [Causal recommendation](#content) \n\n1. **The Deconfounded Recommender: A Causal Inference Approach to Recommendation**, *arXiv*, 2019. [paper](https://arxiv.org/abs/1808.06581) [code](https://github.com/blei-lab/deconfounder_tutorial)\n    \n    Yixin Wang, Dawen Liang, Laurent Charlin, David M. Blei. \n\n1. **The Blessings of Multiple Causes**, *arXiv*, 2019. [paper](https://arxiv.org/abs/1805.06826)\n    \n    Yixin Wang, David M. Blei. \n\n\u003cdetails\u003e\u003csummary\u003e comments \u003c/summary\u003e \n\n3. **Comment: Reflections on the Deconfounder**, *arXiv*, 2019. [paper](https://arxiv.org/abs/1910.08042)\n\n    Alexander D'Amour\n\n1. **On Multi-Cause Causal Inference with Unobserved Confounding: Counterexamples, Impossibility, and Alternatives**, *arXiv*, 2019. [paper](https://arxiv.org/abs/1902.10286)\n\n    Alexander D'Amour\n\n1. **Comment on \"Blessings of Multiple Causes\"**, *arXiv*, 2019. [paper](https://arxiv.org/abs/1910.05438)\n    \n    Elizabeth L. Ogburn, Ilya Shpitser, Eric J. Tchetgen Tchetgen.\n\n1. **The Blessings of Multiple Causes: A Reply to Ogburn et al. (2019)**, *arXiv*, 2019. [paper](https://arxiv.org/abs/1910.07320)\n    \n    Yixin Wang, David M. Blei.\n\n\u003c/details\u003e\n\n7. **Recommendations as Treatments: Debiasing Learning and Evaluation**, *PMLR*, 2016. [paper](http://proceedings.mlr.press/v48/schnabel16.html)\n    \n    Tobias Schnabel, Adith Swaminathan, Ashudeep Singh, Navin Chandak, Thorsten Joachims.\n\n1. **Collaborative Prediction and Ranking with Non-Random Missing Data**, *RecSys*, 2009. [paper](http://www.cs.toronto.edu/~zemel/documents/acmrec2009-MarlinZemel.pdf)\n    \n    Benjamin M. Marlin, Richard S. Zemel.\n\n## [Causal reinforcement learning](#content) \n\n1. **Counterfactual Multi-Agent Policy Gradients**, *AAAI*, 2018. [paper](https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/viewPaper/17193)\n    \n    Jakob N. Foerster, Gregory Farquhar, Triantafyllos Afouras, Nantas Nardelli, Shimon Whiteson. \n\n## [Feature Selection in causal inference](#content)\n\n1. **Ultra-high dimensional variable selection for doubly robust causal inference**, *Biometrics*, 2022. [paper](https://arxiv.org/abs/2007.14190) [code](https://github.com/dingketang/ultra-high-DRCI) [slides](https://drive.google.com/file/d/1OlwNi9eMu_MQe3TyiHpHg2ULdfGD2x0S/view?usp=sharing)\n\n    Dingke Tang, Dehan Kong, Wenliang Pan, Linbo Wang\n\n1. **Outcome‐adaptive lasso: variable selection for causal inference**, *Biometrics* 2017. [paper](https://onlinelibrary.wiley.com/doi/pdf/10.1111/biom.12679?casa_token=_xFuHHhoWlAAAAAA:gKO0JyJC0g54pOfbOVlNew7t1M29UD_A46yJJUAGiLAuxO87p4lGmMneYklKfuWGiHCitIbvKtjfEMAN)  [video](https://crossminds.ai/video/variable-selection-for-causal-inference-outcome-adaptive-lasso-6070a5f9fa08279acdb2124a/)\n\n    Susan M. Shortreed, Ashkan Ertefaie\n\n## [Applications](#content)\n\n### [Social sciences](#content)\n\n1. **Double machine learning-based programme evaluation under unconfoundedness**, *The Econometrics Journal*, 2022. [paper](https://doi.org/10.1093/ectj/utac015)\n    \n    Michael C Knaus.\n\n1. **State-Building through Public Land Disposal? An Application of Matrix Completion for Counterfactual Prediction**, *arXiv*, 2021. [paper](https://arxiv.org/abs/1903.08028) [code](https://github.com/jvpoulos/homesteads)\n    \n    Jason Poulos.\n\n1. **RNN-based counterfactual prediction, with an application to homestead policy and public schooling**, *JRSS-C*, 2021. [paper](http://jasonvpoulos.com/papers/17117351.pdf) [code](https://github.com/jvpoulos/rnns-causal)\n    \n    Jason Poulos, Shuxi Zeng.\n\n1. **Estimating Treatment Effects with Causal Forests: An Application**, *arXiv*, 2019. [paper](https://arxiv.org/abs/1902.07409)\n    \n    Susan Athey, Stefan Wager.\n\n1. **Ensemble Methods for Causal Effects in Panel Data Settings**, *AER P\u0026P*, 2019. [paper](https://arxiv.org/abs/1903.10079)\n    \n    Susan Athey, Mohsen Bayati, Guido W. Imbens, Zhaonan Qu.\n\n### [Text](#content)\n\n1. **Counterfactual Data Augmentation for Neural Machine Translation**, *ACL*, 2021. [paper](https://www.aclweb.org/anthology/2021.naacl-main.18/) [code](https://github.com/xxxiaol/GCI)\n    \n     Qi Liu, Matt Kusner, Phil Blunsom.\n\n1. **Everything Has a Cause: Leveraging Causal Inference in Legal Text Analysis**, *arXIv*, 2021. [paper](https://arxiv.org/abs/2104.09420) [code](https://github.com/xxxiaol/GCI)\n    \n     Xiao Liu, Da Yin, Yansong Feng, Yuting Wu, Dongyan Zhao.\n\n1. **Causal Effects of Linguistic Properties**, *arXIv*, 2021. [paper](https://arxiv.org/abs/2010.12919)\n    \n     Reid Pryzant, Dallas Card, Dan Jurafsky, Victor Veitch, Dhanya Sridhar.\n\n1. **Sketch and Customize: A Counterfactual Story Generator**, *arXIv*, 2021. [paper](https://arxiv.org/abs/2104.00929)\n    \n    Changying Hao, Liang Pang, Yanyan Lan, Yan Wang, Jiafeng Guo, Xueqi Cheng.\n\n1. **Counterfactual Generator: A Weakly-Supervised Method for Named Entity Recognition**, *EMNLP*, 2020. [paper](https://github.com/xijiz/cfgen/blob/master/docs/cfgen.pdf) [code](https://github.com/xijiz/cfgen)\n    \n    Xiangji Zeng, Yunliang Li, Yuchen Zhai, Yin Zhang.\n\n1. **Using Text Embeddings for Causal Inference**, *arXIv*, 2019. [paper](https://arxiv.org/abs/1905.12741) [code](https://github.com/blei-lab/causal-text-embeddings)\n    \n    Victor Veitch, Dhanya Sridhar, David M. Blei.\n\n1. **Counterfactual Story Reasoning and Generation**, *arXIv*, 2019. [paper](https://arxiv.org/abs/1909.04076)\n    \n    Lianhui Qin, Antoine Bosselut, Ari Holtzman, Chandra Bhagavatula, Elizabeth Clark, Yejin Choi.\n\n1. **How to Make Causal Inferences Using Texts**, *arXIv*, 2018. [paper](https://arxiv.org/abs/1802.02163)\n\n    Naoki Egami, Christian J. Fong, Justin Grimmer, Margaret E. Roberts, Brandon M. Stewart.\n\n### [Health](#content)\n\n1. **Targeted learning in observational studies with multi-level treatments: An evaluation of antipsychotic drug treatment safety for patients with serious mental illnesses**, *arXIv*, 2022. [paper](https://arxiv.org/abs/2206.15367) [code](https://github.com/jvpoulos/multi-tmle)\n    \n     Jason Poulos, Marcela Horvitz-Lennon, Katya Zelevinsky, Thomas Huijskens, Pooja Tyagi, Jiaju Yan, Jordi Diaz, Tudor Cristea-Platon, Sharon-Lise Normand.\n\n## [Resources](#content)\n\n### [Workshops](#content)\n\n1. **NeurIPS 2021 Workshop** [link](https://why21.causalai.net/)\n\n1. **UAI 2021 Workshop** [link](https://sites.google.com/uw.edu/causaluai2021/home?authuser=0)\n\n1. **KDD 2021 Workshop** [link](https://bcirwis2021.github.io/cfp.html)\n\n1. **ICML 2021 Workshop** [link](https://sites.google.com/view/naci2021/home)\n\n1. **EMNLP 2021 Workshop** [link](https://causaltext.github.io/2021/)\n\n1. **NeurIPS 2020 Workshop** [link](https://www.cmu.edu/dietrich/causality/neurips20ws/)\n\n1. **NeurIPS 2019 Workshop** [link](http://tripods.cis.cornell.edu/neurips19_causalml/)\n\n1. **NIPS 2018 Workshop** [link](https://sites.google.com/view/nips2018causallearning/home)\n\n1. **NIPS 2017 Workshop** [link](https://sites.google.com/view/causalnips2017)\n\n1. **NIPS 2016 Workshop** [link](https://sites.google.com/site/whatif2016nips/)\n\n1. **NIPS 2013 Workshop** [link](http://clopinet.com/isabelle/Projects/NIPS2013/)\n\n### [Proceedings](#content)\n\n1. **PMLR, Volume 6: Causality: Objectives and Assessment, 12 December 2008, Whistler, Canada** [link](http://proceedings.mlr.press/v6/)\n\n### [Code libraries](#content)\n\n1. **Causal Inference 360: A Python package for inferring causal effects from observational data.** [link](https://github.com/IBM/causallib)\n\n1. **WhyNot: A Python package connecting tools from causal inference and reinforcement learning with a range of complex simulators** [link](https://github.com/zykls/whynot)\n\n1. **EconML: A Python Package for ML-Based Heterogeneous Treatment Effects Estimation** [link](https://github.com/microsoft/EconML)\n\n1. **Uplift modeling and causal inference with machine learning algorithms** [link](https://github.com/uber/causalml)\n\n### [Benchmark datasets](#content)\n\n1. **IHDP, Jobs, and News benchmarks** [link](https://fredjo.com/)\n\n1. **Twins** [link](http://www.nber.org/data/linked-birth-infant-death-data-vitalstatistics-data.htm)\n\n1. **Causality workbench** [link](http://www.causality.inf.ethz.ch/repository.php?page=data)\n\n### [Courses](#content)\n\n1. **CS7792 - Counterfactual Machine Learning** [link](http://www.cs.cornell.edu/courses/cs7792/2016fa/)\n\n1. **Introduction to Causal Inference** [link](https://www.bradyneal.com/causal-inference-course)\n\n1. **Machine Learning \u0026 Causal Inference: A Short Course** [link](https://www.youtube.com/playlist?list=PLxq_lXOUlvQAoWZEqhRqHNezS30lI49G-)\n\n1. **KDD 2020: Lecture Style Tutorials: Casual Inference Meets Machine Learning** [link](https://www.youtube.com/watch?v=DbW2e2q8Gjs)\n\n### [Industry](#content)\n\n1. **Causality and Machine Learning: Microsoft Research** [link](https://www.microsoft.com/en-us/research/group/causal-inference/#!publications)\n\n### [Groups](#content)\n\n1. **Society for Causal Inference** [link](https://sci-info.org/)\n\n1. **Research Laboratory led by Prof. Mihaela van der Schaar** [link](http://www.vanderschaar-lab.com/NewWebsite/causal_inference_and_treatment_effects.html)\n\n### [Lists](#content)\n\n1. **An index of algorithms for learning causality with data** [link](https://github.com/rguo12/awesome-causality-algorithms)\n\n1. **An index of datasets that can be used for learning causality** [link](https://github.com/rguo12/awesome-causality-data)\n\n1. **Papers about Causal Inference and Language** [link](https://github.com/causaltext/causal-text-papers)\n\n### [Books](#content)\n\n1. **Causal Machine Learning** [link](https://www.manning.com/books/causal-machine-learning)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjvpoulos%2Fcausal-ml","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fjvpoulos%2Fcausal-ml","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjvpoulos%2Fcausal-ml/lists"}