/causal-ml

Must-read papers and resources related to causal inference and machine (deep) learning

Must-read recent papers and resources on {Causal}∩{ML}

Contributions are welcome. Inspired by GNNpapers.

1. Surveys
2. Individual treatment effects
2.1. Heterogeneous treatment effects 2.2. Static data
2.3. Temporal data
3. Representation learning
4. Semiparametric / double robust inference
5. Policy learning / causal discovery
6. Causal recommendation
7. Causal reinforcement learning
8. Applications
8.1. Social Sciences 8.2. Text
9. Resources
9.1. Workshops 9.2. Proceedings
9.3. Code libraries 9.4. Benchmark datasets
9.5. Courses 9.6. Industry
9.7. Groups 9.8. Lists
  1. Toward Causal Representation Learning, IEEE, 2021. paper

    Bernhard Schölkopf, Francesco Locatello, Stefan Bauer, Nan Rosemary Ke, Nal Kalchbrenner, Anirudh Goyal, Yoshua Bengio.

  2. A Survey of Learning Causality with Data: Problems and Methods, ACM, 2020. paper

    Ruocheng Guo, Lu Cheng, Jundong Li, P. Richard Hahn, Huan Liu.

  3. Machine learning and causal inference for policy evaluation, KDD, 2015. paper

    Susan Athey.

  1. Can Transformers be Strong Treatment Effect Estimators?, arxiv, 2022. paper code

    Yi-Fan Zhang, Hanlin Zhang, Zachary C. Lipton, Li Erran Li, Eric P. Xing.

  2. Graph Intervention Networks for Causal Effect Estimation, arXiv, 2021. paper code

    Jean Kaddour, Qi Liu, Yuchen Zhu, Matt J. Kusner, Ricardo Silva.

  3. Quasi-Oracle Estimation of Heterogeneous Treatment Effects, arXiv, 2019. paper

    Xinkun Nie, Stefan Wager.

  4. Generalized Random Forests, Annals of Statistics, 2019. paper

    Susan Athey, Julie Tibshirani, Stefan Wager.

  5. Machine Learning Estimation of Heterogeneous Treatment Effects with Instruments, NeurIPS, 2019. paper

    Vasilis Syrgkanis, Victor Lei, Miruna Oprescu, Maggie Hei, Keith Battocchi, Greg Lewis.

  6. Orthogonal Random Forest for Causal Inference, PMLR, 2019. paper

    Miruna Oprescu, Vasilis Syrgkanis, Zhiwei Steven Wu.

  7. Meta-learners for Estimating Heterogeneous Treatment Effects using Machine Learning, PNAS, 2019. paper

    Miruna Oprescu, Vasilis Syrgkanis, Zhiwei Steven Wu.

  8. Machine Learning Analysis of Heterogeneity in the Effect of Student Mindset Interventions, Observational Studies, 2019. paper

    Fredrik D. Johansson.

  9. Estimation and Inference of Heterogeneous Treatment Effects using Random Forests, JASA, 2018. paper

    Stefan Wager, Susan Athey.

  10. Limits of Estimating Heterogeneous Treatment Effects: Guidelines for Practical Algorithm Design, PMLR, 2018. paper

    Ahmed Alaa, Mihaela Schaar.

  11. Transfer Learning for Estimating Causal Effects using Neural Networks, arXiv, 2018. paper

    Sören R. Künzel, Bradly C. Stadie, Nikita Vemuri, Varsha Ramakrishnan, Jasjeet S. Sekhon, Pieter Abbeel.

  12. Recursive partitioning for heterogeneous causal effects, PNAS, 2016. paper

    Susan Athey, Guido Imbens.

  13. Machine Learning Methods for Estimating Heterogeneous Causal Effects, ArXiv, 2015. paper

    Susan Athey, Guido W. Imbens.

  1. VCNet and Functional Targeted Regularization For Learning Causal Effects of Continuous Treatments, ICLR, 2021. paper code

    Lizhen Nie, Mao Ye, Qiang Liu, Dan Nicolae.

  2. Learning Counterfactual Representations for Estimating Individual Dose-Response Curves, AAAI, 2020. paper code

    Patrick Schwab, Lorenz Linhardt, Stefan Bauer, Joachim M. Buhmann, Walter Karlen.

  3. Estimating the Effects of Continuous-valued Interventions using Generative Adversarial Networks, NeurIPS, 2020. paper code

    Ioana Bica, James Jordon, Mihaela van der Schaar.

  4. Learning Individual Causal Effects from Networked Observational Data, WSDM, 2020. paper code

    Ruocheng Guo, Jundong Li, Huan Liu.

  5. Learning Overlapping Representations for the Estimation of Individualized Treatment Effects, AISTATS, 2020. paper

    Yao Zhang, Alexis Bellot, Mihaela van der Schaar.

  6. Adapting Neural Networks for the Estimation of Treatment Effects, arXiv, 2019. paper code

    Claudia Shi, David M. Blei, Victor Veitch.

  7. Program Evaluation and Causal Inference with High-Dimensional Data, arXiv, 2018. paper

    Alexandre Belloni, Victor Chernozhukov, Ivan Fernández-Val, Christian Hansen.

  8. GANITE: Estimation of Individualized Treatment Effects using Generative Adversarial Nets, ICLR, 2018. paper code

    Jinsung Yoon, James Jordon, Mihaela van der Schaar.

  9. Estimation of Individual Treatment Effect in Latent Confounder Models via Adversarial Learning, arXiv, 2018. paper

    Changhee Lee, Nicholas Mastronarde, Mihaela van der Schaar.

  10. Deep IV: A Flexible Approach for Counterfactual Prediction, PMLR, 2017. paper

    Uri Shalit, Fredrik D. Johansson, David Sontag.

  11. Causal Effect Inference with Deep Latent-Variable Models, arXiv, 2017. paper code

    Christos Louizos, Uri Shalit, Joris Mooij, David Sontag, Richard Zemel, Max Welling.

  12. Estimating individual treatment effect: generalization bounds and algorithms, PMLR, 2017. paper code

    Uri Shalit, Fredrik D. Johansson, David Sontag.

  1. Time Series Deconfounder: Estimating Treatment Effects over Time in the Presence of Hidden Confounders, ICML, 2020. paper code

    Ioana Bica, Ahmed M. Alaa, Mihaela van der Schaar.

  2. Estimating Counterfactual Treatment Outcomes over Time through Adversarially Balanced Representations, ICLR, 2020. paper code

    Ioana Bica, Ahmed M. Alaa, James Jordon, Mihaela van der Schaar.

  3. Generative Learning of Counterfactual for Synthetic Control Applications in Econometrics, arXiv, 2019. paper

    Chirag Modi, Uros Seljak.

  4. Robust Synthetic Control, JMLR, 2019. paper

    Muhammad Amjad, Devavrat Shah, Dennis Shen.

  5. ArCo: An artificial counterfactual approach for high-dimensional panel time-series data, Journal of Econometrics, 2018. paper

    Carlos Carvalho, Ricardo Masini, Marcelo C. Medeiros.

  6. Forecasting Treatment Responses Over Time Using Recurrent Marginal Structural Networks, NIPS, 2018. paper code

    Sonali Parbhoo, Stefan Bauer, Patrick Schwab.

  1. Deep Structural Causal Models for Tractable Counterfactual Inference, NeurIPS, 2020. paper code

    Nick Pawlowski, Daniel C. Castro, Ben Glocker.

  2. NCoRE: Neural Counterfactual Representation Learning for Combinations of Treatments, arXiv, 2021. paper

    Sonali Parbhoo, Stefan Bauer, Patrick Schwab.

  3. Perfect Match: A Simple Method for Learning Representations For Counterfactual Inference With Neural Networks, arXiv, 2019. paper code

    Patrick Schwab, Lorenz Linhardt, Walter Karlen.

  4. Representation Learning for Treatment Effect Estimation from Observational Data, NeurIPS, 2019. paper

    Liuyi Yao et al.

  5. Invariant Models for Causal Transfer Learning, JMLR, 2018. paper

    Mateo Rojas-Carulla, Bernhard Schölkopf, Richard Turner, Jonas Peters.

  6. Learning Representations for Counterfactual Inference, arXiv, 2018. paper code

    Fredrik D. Johansson, Uri Shalit, David Sontag.

  1. Sparsity Double Robust Inference of Average Treatment Effects, arXiv, 2019. paper

    Jelena Bradic, Stefan Wager, Yinchu Zhu.

  2. Deep Neural Networks for Estimation and Inference, arXiv, 2019. paper

    Max H. Farrell, Tengyuan Liang, Sanjog Misra.

  3. Approximate Residual Balancing: De-Biased Inference of Average Treatment Effects in High Dimensions, JRSS-B, 2018. paper

    Susan Athey, Guido W. Imbens, Stefan Wager.

  4. Deep Counterfactual Networks with Propensity-Dropout, arXiv, 2017. paper

    Ahmed M. Alaa, Michael Weisz, Mihaela van der Schaar.

  5. Double/Debiased Machine Learning for Treatment and Causal Parameters, arXiv, 2017. paper

    Victor Chernozhukov, Denis Chetverikov, Mert Demirer, Esther Duflo, Christian Hansen, Whitney Newey, James Robins.

  6. Doubly Robust Policy Evaluation and Optimization, Statistical Science, 2014. paper

    Miroslav Dudík, Dumitru Erhan, John Langford, Lihong Li.

  1. Differentiable Causal Discovery Under Unmeasured Confounding, arXiv, 2021. paper

    Rohit Bhattacharya, Tushar Nagarajan, Daniel Malinsky, Ilya Shpitser.

  2. Causal Discovery with Attention-Based Convolutional Neural Networks, Machine Learning and Knowledge Extraction, 2019. paper code

    Meike Nauta, Doina Bucur, Christin Seifert.

  3. A Meta-Transfer Objective for Learning to Disentangle Causal Mechanisms, arXiv, 2019. paper

    Yoshua Bengio, Tristan Deleu, Nasim Rahaman, Rosemary Ke, Sébastien Lachapelle, Olexa Bilaniuk, Anirudh Goyal, Christopher Pal.

  4. Causal Discovery with Reinforcement Learning, arXiv, 2019. paper

    Shengyu Zhu, Zhitang Chen.

  5. CausalGAN: Learning Causal Implicit Generative Models with Adversarial Training, arXiv, 2019. paper

    Murat Kocaoglu, Christopher Snyder, Alexandros G. Dimakis, Sriram Vishwanath.

  6. Learning When-to-Treat Policies, arXiv, 2019. paper

    Xinkun Nie, Emma Brunskill, Stefan Wager.

  7. Learning Neural Causal Models from Unknown Interventions, arXiv, 2019. paper code

    Nan Rosemary Ke, Olexa Bilaniuk, Anirudh Goyal, Stefan Bauer, Hugo Larochelle, Chris Pal, Yoshua Bengio.

  8. Counterfactual Policy Optimization Using Domain-Adversarial Neural Networks, ICML, 2018. paper

    Onur Atan, William R. Zame, Mihaela van der Schaar.

  9. Causal Bandits: Learning Good Interventions via Causal Inference, NIPS, 2016. paper

    Finnian Lattimore, Tor Lattimore, Mark D. Reid.

  10. Counterfactual Risk Minimization: Learning from Logged Bandit Feedback, arXiv, 2015. paper

    Adith Swaminathan, Thorsten Joachims.

  1. The Deconfounded Recommender: A Causal Inference Approach to Recommendation, arXiv, 2019. paper code

    Yixin Wang, Dawen Liang, Laurent Charlin, David M. Blei.

  2. The Blessings of Multiple Causes, arXiv, 2019. paper

    Yixin Wang, David M. Blei.

comments
  1. Comment: Reflections on the Deconfounder, arXiv, 2019. paper

    Alexander D'Amour

  2. On Multi-Cause Causal Inference with Unobserved Confounding: Counterexamples, Impossibility, and Alternatives, arXiv, 2019. paper

    Alexander D'Amour

  3. Comment on "Blessings of Multiple Causes", arXiv, 2019. paper

    Elizabeth L. Ogburn, Ilya Shpitser, Eric J. Tchetgen Tchetgen.

  4. The Blessings of Multiple Causes: A Reply to Ogburn et al. (2019), arXiv, 2019. paper

    Yixin Wang, David M. Blei.

  1. Recommendations as Treatments: Debiasing Learning and Evaluation, PMLR, 2016. paper

    Tobias Schnabel, Adith Swaminathan, Ashudeep Singh, Navin Chandak, Thorsten Joachims.

  2. Collaborative Prediction and Ranking with Non-Random Missing Data, RecSys, 2009. paper

    Benjamin M. Marlin, Richard S. Zemel.

  1. Counterfactual Multi-Agent Policy Gradients, AAAI, 2018. paper

    Jakob N. Foerster, Gregory Farquhar, Triantafyllos Afouras, Nantas Nardelli, Shimon Whiteson.

  1. Retrospective causal inference via matrix completion, with an evaluation of the effect of European integration on cross-border employment, arXiv, 2021. paper code

    Jason Poulos, Andrea Albanese, Andrea Mercatanti, Fan Li.

  2. RNN-based counterfactual prediction, with an application to homestead policy and public schooling, JRSS-C, 2021. paper code

    Jason Poulos, Shuxi Zeng.

  3. Estimating Treatment Effects with Causal Forests: An Application, arXiv, 2019. paper

    Susan Athey, Stefan Wager.

  4. Ensemble Methods for Causal Effects in Panel Data Settings, AER P&P, 2019. paper

    Susan Athey, Mohsen Bayati, Guido W. Imbens, Zhaonan Qu.

  1. Counterfactual Data Augmentation for Neural Machine Translation, ACL, 2021. paper code

    Qi Liu, Matt Kusner, Phil Blunsom.

  2. Everything Has a Cause: Leveraging Causal Inference in Legal Text Analysis, arXIv, 2021. paper code

    Xiao Liu, Da Yin, Yansong Feng, Yuting Wu, Dongyan Zhao.

  3. Causal Effects of Linguistic Properties, arXIv, 2021. paper

    Reid Pryzant, Dallas Card, Dan Jurafsky, Victor Veitch, Dhanya Sridhar.

  4. Sketch and Customize: A Counterfactual Story Generator, arXIv, 2021. paper

    Changying Hao, Liang Pang, Yanyan Lan, Yan Wang, Jiafeng Guo, Xueqi Cheng.

  5. Counterfactual Generator: A Weakly-Supervised Method for Named Entity Recognition, EMNLP, 2020. paper code

    Xiangji Zeng, Yunliang Li, Yuchen Zhai, Yin Zhang.

  6. Using Text Embeddings for Causal Inference, arXIv, 2019. paper code

    Victor Veitch, Dhanya Sridhar, David M. Blei.

  7. Counterfactual Story Reasoning and Generation, arXIv, 2019. paper

    Lianhui Qin, Antoine Bosselut, Ari Holtzman, Chandra Bhagavatula, Elizabeth Clark, Yejin Choi.

  8. How to Make Causal Inferences Using Texts, arXIv, 2018. paper

    Naoki Egami, Christian J. Fong, Justin Grimmer, Margaret E. Roberts, Brandon M. Stewart.

  1. NeurIPS 2021 Workshop link

  2. UAI 2021 Workshop link

  3. KDD 2021 Workshop link

  4. ICML 2021 Workshop link

  5. EMNLP 2021 Workshop link

  6. NeurIPS 2020 Workshop link

  7. NeurIPS 2019 Workshop link

  8. NIPS 2018 Workshop link

  9. NIPS 2017 Workshop link

  10. NIPS 2016 Workshop link

  11. NIPS 2013 Workshop link

  1. PMLR, Volume 6: Causality: Objectives and Assessment, 12 December 2008, Whistler, Canada link
  1. Causal Inference 360: A Python package for inferring causal effects from observational data. link

  2. WhyNot: A Python package connecting tools from causal inference and reinforcement learning with a range of complex simulators link

  3. EconML: A Python Package for ML-Based Heterogeneous Treatment Effects Estimation link

  4. Uplift modeling and causal inference with machine learning algorithms link

  1. IHDP, Jobs, and News benchmarks link

  2. Twins link

  3. Causality workbench link

  1. CS7792 - Counterfactual Machine Learning link

  2. Introduction to Causal Inference link

  1. Causality and Machine Learning: Microsoft Research link
  1. Society for Causal Inference link

  2. Research Laboratory led by Prof. Mihaela van der Schaar link

  1. An index of algorithms for learning causality with data link

  2. An index of datasets that can be used for learning causality link

  3. Papers about Causal Inference and Language link