zinccat / 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.

Content

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

Survey papers

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.

Individual treatment effects

Heterogeneous treatment effects

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

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

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

   Xinkun Nie, Stefan Wager.

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

   Susan Athey, Julie Tibshirani, Stefan Wager.

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

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

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

   Miruna Oprescu, Vasilis Syrgkanis, Zhiwei Steven Wu.

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

   Miruna Oprescu, Vasilis Syrgkanis, Zhiwei Steven Wu.

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

   Fredrik D. Johansson.

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

   Stefan Wager, Susan Athey.

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

   Ahmed Alaa, Mihaela Schaar.

10. 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.

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

    Susan Athey, Guido Imbens.

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

    Susan Athey, Guido W. Imbens.

Static data

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.

Temporal data

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.

Representation learning

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.

Semiparametric / double robust inference

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.

Policy learning / causal discovery

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.

Causal recommendation

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

3. Comment: Reflections on the Deconfounder, arXiv, 2019. paper

   Alexander D'Amour

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

   Alexander D'Amour

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

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

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

   Yixin Wang, David M. Blei.

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

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

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

   Benjamin M. Marlin, Richard S. Zemel.

Causal reinforcement learning

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

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

Applications

Social sciences

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.

Text

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.

Resources

Workshops

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

Proceedings

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

Code libraries

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

Benchmark datasets

1. IHDP, Jobs, and News benchmarks link

2. Twins link

3. Causality workbench link

Courses

1. CS7792 - Counterfactual Machine Learning link

2. Introduction to Causal Inference link

Industry

1. Causality and Machine Learning: Microsoft Research link

Groups

1. Society for Causal Inference link

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

Lists

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