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https://github.com/jvpoulos/causal-ml

Must-read papers and resources related to causal inference and machine (deep) learning
https://github.com/jvpoulos/causal-ml
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
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Must-read papers and resources related to causal inference and machine (deep) learning
Host: GitHub
URL: https://github.com/jvpoulos/causal-ml
Owner: jvpoulos
Created: 2019-10-13T23:39:10.000Z (over 4 years ago)
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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
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awesome-interesting-topics-in-nlp - ML
awesome-causality-in-nlp - jvpoulos/causal-ml
README

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

Contributions are welcome. Inspired by [GNNpapers](https://github.com/thunlp/GNNPapers).

## [Content](#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. Feature selection in causal inference

9. Applications

     9.1. Social Sciences

     9.2. Text

 

     9.3. Health

 

10. Resources 

     10.1. Workshops

     10.2. Proceedings

 

     10.3. Code libraries

     10.4. Benchmark datasets

 

     10.5. Courses

     10.6. Industry

 

     10.7. Groups

     10.8. Lists

 

     10.9. Books

 

## [Survey papers](#content)

1. **Causal Machine Learning: A Survey and Open Problems**, 2022. [paper](https://arxiv.org/abs/2206.15475)

    Jean Kaddour, Aengus Lynch, Qi Liu, Matt J. Kusner, Ricardo Silva.

	

1. **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)

    Weijia Zhang, Jiuyong Li, Lin Liu.

1. **Toward Causal Representation Learning**, *IEEE*, 2021. [paper](https://ieeexplore.ieee.org/abstract/document/9363924)

    

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

1. **A Survey of Learning Causality with Data: Problems and Methods**, *ACM*, 2020. [paper](https://arxiv.org/abs/1809.09337)

    

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

1. **Machine learning and causal inference for policy evaluation**, *KDD*, 2015. [paper](https://dl.acm.org/citation.cfm?id=2785466)

    

    Susan Athey.

## [Individual treatment effects](#content) 

### [Heterogeneous treatment effects](#content)  

1. **Can Transformers be Strong Treatment Effect Estimators?**, *arxiv*, 2022. [paper](https://arxiv.org/abs/2202.01336) [code](https://github.com/hlzhang109/TransTEE)

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

1. **Nonparametric Estimation of Heterogeneous Treatment Effects: From Theory to Learning Algorithms**, *AISTATS*, 2021. [paper](https://proceedings.mlr.press/v130/curth21a.html)

    

    Alicia Curth, Mihaela van der Schaar.

1. **Causal Effect Inference for Structured Treatments**, *NeurIPS*, 2021. [paper](https://arxiv.org/abs/2106.01939) [code](https://github.com/JeanKaddour/SIN)

    

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

	

1. **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)

    

    Weijia Zhang, Lin Liu, Jiuyong Li.

1. **Generic Machine Learning Inference on Heterogenous Treatment Effects in Randomized Experiments**, *arXiv*, 2020. [paper](https://arxiv.org/abs/1712.04802)

    Victor Chernozhukov, Mert Demirer, Esther Duflo, Iván Fernández-Val.

1. **Quasi-Oracle Estimation of Heterogeneous Treatment Effects**, *arXiv*, 2019. [paper](https://arxiv.org/abs/1712.04912)

    Xinkun Nie, Stefan Wager.

1. **Generalized Random Forests**, *Annals of Statistics*, 2019. [paper](https://arxiv.org/abs/1610.01271)

    Susan Athey, Julie Tibshirani, Stefan Wager.

1. **Machine Learning Estimation of Heterogeneous Treatment Effects with Instruments**, *NeurIPS*, 2019. [paper](https://arxiv.org/abs/1905.10176)

    

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

1. **Orthogonal Random Forest for Causal Inference**, *PMLR*, 2019. [paper](http://proceedings.mlr.press/v97/oprescu19a.html)

    Miruna Oprescu, Vasilis Syrgkanis, Zhiwei Steven Wu.

1. **Meta-learners for Estimating Heterogeneous Treatment Effects using Machine Learning**, *PNAS*, 2019. [paper](https://arxiv.org/abs/1706.03461)

    Sören R. Künzel, Jasjeet S. Sekhon, Peter J. Bickel, Bin Yu.

1. **Machine Learning Analysis of Heterogeneity in the Effect of Student Mindset Interventions**, *Observational Studies*, 2019. [paper](https://arxiv.org/abs/1811.05975)

    

    Fredrik D. Johansson.

1. **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)

    

    Stefan Wager, Susan Athey.

1. **Limits of Estimating Heterogeneous Treatment Effects: Guidelines for Practical Algorithm Design**, *PMLR*, 2018. [paper](http://proceedings.mlr.press/v80/alaa18a.html)

    

    Ahmed Alaa, Mihaela Schaar.

1. **Transfer Learning for Estimating Causal Effects using Neural Networks**, *arXiv*, 2018. [paper](https://arxiv.org/abs/1808.07804)

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

1. **Recursive partitioning for heterogeneous causal effects**, *PNAS*, 2016. [paper](https://www.pnas.org/content/113/27/7353)

    

    Susan Athey, Guido Imbens.

1. **Machine Learning Methods for Estimating Heterogeneous Causal Effects**, *ArXiv*, 2015. [paper](https://arxiv.org/abs/1504.01132v1)

    Susan Athey, Guido W. Imbens.

### [Static data](#content) 

1. **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)

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

1. **Learning Counterfactual Representations for Estimating Individual Dose-Response Curves**, *AAAI*, 2020. [paper](https://arxiv.org/abs/1902.00981) [code](https://github.com/d909b/drnet)

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

1. **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)

    Ioana Bica, James Jordon, Mihaela van der Schaar.

1. **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)

    Ruocheng Guo, Jundong Li, Huan Liu.

1. **Learning Overlapping Representations for the Estimation of Individualized Treatment Effects**, *AISTATS*, 2020. [paper](https://arxiv.org/abs/2001.04754)

    Yao Zhang, Alexis Bellot, Mihaela van der Schaar.

1. **Adapting Neural Networks for the Estimation of Treatment Effects**, *arXiv*, 2019. [paper](https://arxiv.org/abs/1906.02120) [code](http://github.com/claudiashi57/dragonnet)

    

    Claudia Shi, David M. Blei, Victor Veitch.

1. **Program Evaluation and Causal Inference with High-Dimensional Data**, *arXiv*, 2018. [paper](https://arxiv.org/abs/1311.2645)

    

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

1. **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)

    

    Jinsung Yoon, James Jordon, Mihaela van der Schaar.

1. **Estimation of Individual Treatment Effect in Latent Confounder Models via Adversarial Learning**, *arXiv*, 2018. [paper](https://arxiv.org/abs/1811.08943)

    

    Changhee Lee, Nicholas Mastronarde, Mihaela van der Schaar.

1. **Deep IV: A Flexible Approach for Counterfactual Prediction**, *PMLR*, 2017. [paper](http://proceedings.mlr.press/v70/hartford17a.html)

    

    Uri Shalit, Fredrik D. Johansson, David Sontag.

1. **Causal Effect Inference with Deep Latent-Variable Models**, *arXiv*, 2017. [paper](https://arxiv.org/abs/1705.08821) [code](https://github.com/AMLab-Amsterdam/CEVAE)

    

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

1. **Estimating individual treatment effect: generalization bounds and algorithms**, *PMLR*, 2017. [paper](http://proceedings.mlr.press/v70/shalit17a.html) [code](https://github.com/clinicalml/cfrnet)

    

    Uri Shalit, Fredrik D. Johansson, David Sontag.

### [Temporal data](#content) 

1. **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)

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

1. **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)

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

1. **Generative Learning of Counterfactual for Synthetic Control Applications in Econometrics**, *arXiv*, 2019. [paper](https://arxiv.org/abs/1910.07178)

    

    Chirag Modi, Uros Seljak.

1. **Robust Synthetic Control**, *JMLR*, 2019. [paper](http://www.jmlr.org/papers/volume19/17-777.pdf)

    

    Muhammad Amjad, Devavrat Shah, Dennis Shen.

1. **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)

    

    Carlos Carvalho, Ricardo Masini, Marcelo C. Medeiros.

1. **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)

    

    Sonali Parbhoo, Stefan Bauer, Patrick Schwab.

## [Representation learning](#content)   

1. **Deep Structural Causal Models for Tractable Counterfactual Inference**, *NeurIPS*, 2020. [paper](https://arxiv.org/abs/2006.06485) [code](https://github.com/biomedia-mira/deepscm)

    Nick Pawlowski, Daniel C. Castro, Ben Glocker.

1. **NCoRE: Neural Counterfactual Representation Learning for Combinations of Treatments**, *arXiv*, 2021. [paper](https://arxiv.org/abs/2103.11175)

    

    Sonali Parbhoo, Stefan Bauer, Patrick Schwab.

1. **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)

    

    Patrick Schwab, Lorenz Linhardt, Walter Karlen.

1. **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) 

    

    Liuyi Yao et al.

1. **Invariant Models for Causal Transfer Learning**, *JMLR*, 2018. [paper](http://jmlr.org/papers/v19/16-432.html) 

    

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

1. **Learning Representations for Counterfactual Inference**, *arXiv*, 2018. [paper](https://arxiv.org/abs/1605.03661) [code](https://github.com/clinicalml/cfrnet)

    

    Fredrik D. Johansson, Uri Shalit, David Sontag.

## [Semiparametric / double robust inference](#content)  

1. **Sparsity Double Robust Inference of Average Treatment Effects**, *arXiv*, 2019. [paper](https://arxiv.org/abs/1905.00744)

    

    Jelena Bradic, Stefan Wager, Yinchu Zhu.

1. **Deep Neural Networks for Estimation and Inference**, *arXiv*, 2019. [paper](https://arxiv.org/abs/1809.09953)

    

    Max H. Farrell, Tengyuan Liang, Sanjog Misra.

1. **Deep Counterfactual Networks with Propensity-Dropout**, *arXiv*, 2017. [paper](https://arxiv.org/abs/1706.05966)

    

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

1. **Double/Debiased Machine Learning for Treatment and Causal Parameters**, *arXiv*, 2017. [paper](https://arxiv.org/abs/1608.00060)

    

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

1. **Doubly Robust Policy Evaluation and Optimization**, *Statistical Science*, 2014. [paper](https://arxiv.org/abs/1503.02834)

    

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

## [Policy learning / causal discovery](#content)  

1. **Differentiable Causal Discovery Under Unmeasured Confounding**, *arXiv*, 2021. [paper](https://arxiv.org/abs/2010.06978)

    

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

1. **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)

    

    Meike Nauta, Doina Bucur, Christin Seifert.

1. **A Meta-Transfer Objective for Learning to Disentangle Causal Mechanisms**, *arXiv*, 2019. [paper](https://arxiv.org/abs/1901.10912)

    

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

1. **Causal Discovery with Reinforcement Learning**, *arXiv*, 2019. [paper](https://arxiv.org/abs/1906.04477)

    

    Shengyu Zhu, Zhitang Chen.

1. **CausalGAN: Learning Causal Implicit Generative Models with Adversarial Training**, *arXiv*, 2019. [paper](https://arxiv.org/abs/1709.02023)

    

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

1. **Learning When-to-Treat Policies**, *arXiv*, 2019. [paper](https://arxiv.org/abs/1905.09751)

    

    Xinkun Nie, Emma Brunskill, Stefan Wager.

1. **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)

    

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

1. **Counterfactual Policy Optimization Using Domain-Adversarial Neural Networks**, *ICML*, 2018. [paper](http://medianetlab.ee.ucla.edu/papers/cf_treat_v5)

    

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

1. **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)

    

    Finnian Lattimore, Tor Lattimore, Mark D. Reid.

1. **Counterfactual Risk Minimization: Learning from Logged Bandit Feedback**, *arXiv*, 2015. [paper](https://arxiv.org/abs/1502.02362)

    

    Adith Swaminathan, Thorsten Joachims.

## [Causal recommendation](#content) 

1. **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)

    

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

1. **The Blessings of Multiple Causes**, *arXiv*, 2019. [paper](https://arxiv.org/abs/1805.06826)

    

    Yixin Wang, David M. Blei. 

 comments  

3. **Comment: Reflections on the Deconfounder**, *arXiv*, 2019. [paper](https://arxiv.org/abs/1910.08042)

    Alexander D'Amour

1. **On Multi-Cause Causal Inference with Unobserved Confounding: Counterexamples, Impossibility, and Alternatives**, *arXiv*, 2019. [paper](https://arxiv.org/abs/1902.10286)

    Alexander D'Amour

1. **Comment on "Blessings of Multiple Causes"**, *arXiv*, 2019. [paper](https://arxiv.org/abs/1910.05438)

    

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

1. **The Blessings of Multiple Causes: A Reply to Ogburn et al. (2019)**, *arXiv*, 2019. [paper](https://arxiv.org/abs/1910.07320)

    

    Yixin Wang, David M. Blei.

7. **Recommendations as Treatments: Debiasing Learning and Evaluation**, *PMLR*, 2016. [paper](http://proceedings.mlr.press/v48/schnabel16.html)

    

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

1. **Collaborative Prediction and Ranking with Non-Random Missing Data**, *RecSys*, 2009. [paper](http://www.cs.toronto.edu/~zemel/documents/acmrec2009-MarlinZemel.pdf)

    

    Benjamin M. Marlin, Richard S. Zemel.

## [Causal reinforcement learning](#content) 

1. **Counterfactual Multi-Agent Policy Gradients**, *AAAI*, 2018. [paper](https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/viewPaper/17193)

    

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

## [Feature Selection in causal inference](#content)

1. **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)

    Dingke Tang, Dehan Kong, Wenliang Pan, Linbo Wang

1. **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/)

    Susan M. Shortreed, Ashkan Ertefaie

## [Applications](#content)

### [Social sciences](#content)

1. **Double machine learning-based programme evaluation under unconfoundedness**, *The Econometrics Journal*, 2022. [paper](https://doi.org/10.1093/ectj/utac015)

    

    Michael C Knaus.

1. **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)

    

    Jason Poulos.

1. **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)

    

    Jason Poulos, Shuxi Zeng.

1. **Estimating Treatment Effects with Causal Forests: An Application**, *arXiv*, 2019. [paper](https://arxiv.org/abs/1902.07409)

    

    Susan Athey, Stefan Wager.

1. **Ensemble Methods for Causal Effects in Panel Data Settings**, *AER P&P*, 2019. [paper](https://arxiv.org/abs/1903.10079)

    

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

### [Text](#content)

1. **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)

    

     Qi Liu, Matt Kusner, Phil Blunsom.

1. **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)

    

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

1. **Causal Effects of Linguistic Properties**, *arXIv*, 2021. [paper](https://arxiv.org/abs/2010.12919)

    

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

1. **Sketch and Customize: A Counterfactual Story Generator**, *arXIv*, 2021. [paper](https://arxiv.org/abs/2104.00929)

    

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

1. **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)

    

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

1. **Using Text Embeddings for Causal Inference**, *arXIv*, 2019. [paper](https://arxiv.org/abs/1905.12741) [code](https://github.com/blei-lab/causal-text-embeddings)

    

    Victor Veitch, Dhanya Sridhar, David M. Blei.

1. **Counterfactual Story Reasoning and Generation**, *arXIv*, 2019. [paper](https://arxiv.org/abs/1909.04076)

    

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

1. **How to Make Causal Inferences Using Texts**, *arXIv*, 2018. [paper](https://arxiv.org/abs/1802.02163)

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

### [Health](#content)

1. **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)

    

     Jason Poulos, Marcela Horvitz-Lennon, Katya Zelevinsky, Thomas Huijskens, Pooja Tyagi, Jiaju Yan, Jordi Diaz, Tudor Cristea-Platon, Sharon-Lise Normand.

## [Resources](#content)

### [Workshops](#content)

1. **NeurIPS 2021 Workshop** [link](https://why21.causalai.net/)

1. **UAI 2021 Workshop** [link](https://sites.google.com/uw.edu/causaluai2021/home?authuser=0)

1. **KDD 2021 Workshop** [link](https://bcirwis2021.github.io/cfp.html)

1. **ICML 2021 Workshop** [link](https://sites.google.com/view/naci2021/home)

1. **EMNLP 2021 Workshop** [link](https://causaltext.github.io/2021/)

1. **NeurIPS 2020 Workshop** [link](https://www.cmu.edu/dietrich/causality/neurips20ws/)

1. **NeurIPS 2019 Workshop** [link](http://tripods.cis.cornell.edu/neurips19_causalml/)

1. **NIPS 2018 Workshop** [link](https://sites.google.com/view/nips2018causallearning/home)

1. **NIPS 2017 Workshop** [link](https://sites.google.com/view/causalnips2017)

1. **NIPS 2016 Workshop** [link](https://sites.google.com/site/whatif2016nips/)

1. **NIPS 2013 Workshop** [link](http://clopinet.com/isabelle/Projects/NIPS2013/)

### [Proceedings](#content)

1. **PMLR, Volume 6: Causality: Objectives and Assessment, 12 December 2008, Whistler, Canada** [link](http://proceedings.mlr.press/v6/)

### [Code libraries](#content)

1. **Causal Inference 360: A Python package for inferring causal effects from observational data.** [link](https://github.com/IBM/causallib)

1. **WhyNot: A Python package connecting tools from causal inference and reinforcement learning with a range of complex simulators** [link](https://github.com/zykls/whynot)

1. **EconML: A Python Package for ML-Based Heterogeneous Treatment Effects Estimation** [link](https://github.com/microsoft/EconML)

1. **Uplift modeling and causal inference with machine learning algorithms** [link](https://github.com/uber/causalml)

### [Benchmark datasets](#content)

1. **IHDP, Jobs, and News benchmarks** [link](https://fredjo.com/)

1. **Twins** [link](http://www.nber.org/data/linked-birth-infant-death-data-vitalstatistics-data.htm)

1. **Causality workbench** [link](http://www.causality.inf.ethz.ch/repository.php?page=data)

### [Courses](#content)

1. **CS7792 - Counterfactual Machine Learning** [link](http://www.cs.cornell.edu/courses/cs7792/2016fa/)

1. **Introduction to Causal Inference** [link](https://www.bradyneal.com/causal-inference-course)

1. **Machine Learning & Causal Inference: A Short Course** [link](https://www.youtube.com/playlist?list=PLxq_lXOUlvQAoWZEqhRqHNezS30lI49G-)

1. **KDD 2020: Lecture Style Tutorials: Casual Inference Meets Machine Learning** [link](https://www.youtube.com/watch?v=DbW2e2q8Gjs)

### [Industry](#content)

1. **Causality and Machine Learning: Microsoft Research** [link](https://www.microsoft.com/en-us/research/group/causal-inference/#!publications)

### [Groups](#content)

1. **Society for Causal Inference** [link](https://sci-info.org/)

1. **Research Laboratory led by Prof. Mihaela van der Schaar** [link](http://www.vanderschaar-lab.com/NewWebsite/causal_inference_and_treatment_effects.html)

### [Lists](#content)

1. **An index of algorithms for learning causality with data** [link](https://github.com/rguo12/awesome-causality-algorithms)

1. **An index of datasets that can be used for learning causality** [link](https://github.com/rguo12/awesome-causality-data)

1. **Papers about Causal Inference and Language** [link](https://github.com/causaltext/causal-text-papers)

### [Books](#content)

1. **Causal Machine Learning** [link](https://www.manning.com/books/causal-machine-learning)