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Must-read papers and resources related to causal inference and machine (deep) learning
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Must-read recent papers and resources on {Causal}∩{ML}

Contributions are welcome. Inspired by GNNpapers.


1. Surveys
2. Counterfactual prediction / individual treatment effects
3. Representation learning
4. Dimensionality reduction / regression adjustment
5. Heterogeneous treatment effects
6. Missing data imputation
7. Semiparametric / double robust inference
8. Policy learning / causal discovery
9. Causal recommendation
10. Applications
10.1. Social Sciences 10.2. Text
11. Resources
11.1. Workshops 11.2. Proceedings
11.3. Code libraries 11.4. Benchmark datasets
11.5. Courses 11.6. Industry

Survey papers

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

    Susan Athey.

Counterfactual prediction / individual treatment effects

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

    Chirag Modi, Uros Seljak.

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

    Claudia Shi, David M. Blei, Victor Veitch.

  3. RNN-based counterfactual prediction, arXiv, 2019. paper code

    Jason Poulos.

  4. Matrix Completion Methods for Causal Panel Data Models, arXiv, 2018. paper

    Susan Athey, Mohsen Bayati, Nikolay Doudchenko, Guido Imbens, Khashayar Khosravi.

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

    Bryan Lim, Ahmed Alaa, Mihaela van der Schaar.

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

    Jinsung Yoon, James Jordon, Mihaela van der Schaar.

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

    Changhee Lee, Nicholas Mastronarde, Mihaela van der Schaar.

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

    Uri Shalit, Fredrik D. Johansson, David Sontag.

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

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

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

    Uri Shalit, Fredrik D. Johansson, David Sontag.

Representation learning

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

    Liuyi Yao et al.

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

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

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

    Fredrik D. Johansson, Uri Shalit, David Sontag.

Dimensionality reduction / regression adjustment

  1. Robust Synthetic Control, JMLR, 2019. paper

    Muhammad Amjad, Devavrat Shah, Dennis Shen.

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

    Carlos Carvalho, Ricardo Masini, Marcelo C. Medeiros.

  3. Lasso adjustments of treatment effect estimates in randomized experiments, PNAS, 2016. paper

    Adam Bloniarz, Hanzhong Liu, Cun-Hui Zhang, Jasjeet S. Sekhon, Bin Yu.

Heterogeneous treatment effects

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

    Susan Athey, Julie Tibshirani, Stefan Wager.

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

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

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

    Miruna Oprescu, Vasilis Syrgkanis, Zhiwei Steven Wu.

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

    Miruna Oprescu, Vasilis Syrgkanis, Zhiwei Steven Wu.

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

    Fredrik D. Johansson.

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

    Stefan Wager, Susan Athey.

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

    Ahmed Alaa, Mihaela Schaar.

  8. 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.

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

    Susan Athey, Guido Imbens.

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

    Susan Athey, Guido W. Imbens.

Missing data imputation

  1. NAOMI: Non-Autoregressive Multiresolution Sequence Imputation, arXiv, 2019. paper

    Yukai Liu, Rose Yu, Stephan Zheng, Eric Zhan, Yisong Yue.

  2. GAIN: Missing Data Imputation using Generative Adversarial Nets, ICML, 2018. paper code

    Jinsung Yoon, James Jordon, Mihaela van der Schaar.

  3. MIDA: Multiple Imputation using Denoising Autoencoders, arXiv, 2018. paper code

    Lovedeep Gondara, Ke Wang.

  4. Recurrent Neural Networks for Multivariate Time Series with Missing Values, Scientific Reports, 2018. paper code

    Zhengping Che, Sanjay Purushotham, Kyunghyun Cho, David Sontag, Yan Liu.

  5. BRITS: Bidirectional Recurrent Imputation for Time Series, NeurIPS, 2018. paper

    Wei Cao et al.

  6. Estimating Missing Data in Temporal Data Streams Using Multi-directional Recurrent Neural Networks, arXiv, 2017. paper code

    Jinsung Yoon, William R. Zame, Mihaela van der Schaar.

  7. Geometric Matrix Completion with Recurrent Multi-Graph Neural Networks, NeurIPS, 2017. paper code

    Federico Monti, Michael M. Bronstein, Xavier Bresson.

  8. Modeling Missing Data in Clinical Time Series with RNNs, JMLR, 2016. paper

    Zachary C. Lipton, David C. Kale, Randall Wetzel.

Semiparametric / double robust inference

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

    Jelena Bradic, Stefan Wager, Yinchu Zhu.

  2. Time Series Deconfounder: Estimating Treatment Effects over Time in the Presence of Hidden Confounders, arXiv, 2019. paper

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

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

    Max H. Farrell, Tengyuan Liang, Sanjog Misra.

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

    Susan Athey, Guido W. Imbens, Stefan Wager.

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

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

  6. 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.

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

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

Policy learning / causal discovery

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

    Shengyu Zhu, Zhitang Chen.

  2. Adversarial Generalized Method of Moments, arXiv, 2019. paper code

    Greg Lewis, Vasilis Syrgkanis.

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

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

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

    Xinkun Nie, Emma Brunskill, Stefan Wager.

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

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

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

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

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

    Finnian Lattimore, Tor Lattimore, Mark D. Reid.

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

    Adith Swaminathan, Thorsten Joachims.

Causal recommendation

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

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

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

    Yixin Wang, David M. Blei.

  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.


Social sciences

  1. State-Building through Public Land Disposal? An Application of Matrix Completion for Counterfactual Prediction, arXiv, 2019. paper code

    Jason Poulos.

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

    Susan Athey, Stefan Wager.

  3. 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 Story Reasoning and Generation, arXIv, 2019. paper

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

  2. 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 2019 Workshop link

  2. NIPS 2018 Workshop link

  3. NIPS 2017 Workshop links

  4. NIPS 2016 Workshop link

  5. NIPS 2013 Workshop link


  1. PMLR, Volume 6: Causality: Objectives and Assessment, 12 December 2008, Whistler, Canada link

Code libraries

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

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

Benchmark datasets

  1. IHDP, Jobs, and News benchmarks link

  2. Twins link

  3. Causality workbench link


  1. CS7792 - Counterfactual Machine Learning link


  1. Causality and Machine Learning: Microsoft Research [link]!publications
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