CausalAI

Causal AI

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

1. Causal Machine Learning: A Survey and Open Problems, 2022. paper

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

2. A Unified Survey of Heterogeneous Treatment Effect Estimation and Uplift Modeling, ACM Computing Surveys, 2022. paper

   Weijia Zhang, Jiuyong Li, Lin Liu.

3. Toward Causal Representation Learning, IEEE, 2021. paper

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

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

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

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

   Susan Athey.

Individual treatment effects

Heterogeneous treatment effects

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. Nonparametric Estimation of Heterogeneous Treatment Effects: From Theory to Learning Algorithms, AISTATS, 2021. paper

   Alicia Curth, Mihaela van der Schaar.

3. Causal Effect Inference for Structured Treatments, NeurIPS, 2021. paper code

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

4. Treatment Effect Estimation with Disentangled Latent Factors, AAAI, 2021. paper code

   Weijia Zhang, Lin Liu, Jiuyong Li.

5. Generic Machine Learning Inference on Heterogenous Treatment Effects in Randomized Experiments, arXiv, 2020. paper

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

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

   Xinkun Nie, Stefan Wager.

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

   Susan Athey, Julie Tibshirani, Stefan Wager.

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

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

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

   Miruna Oprescu, Vasilis Syrgkanis, Zhiwei Steven Wu.

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

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

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

    Fredrik D. Johansson.

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

    Stefan Wager, Susan Athey.

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

    Ahmed Alaa, Mihaela Schaar.

14. 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.

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

    Susan Athey, Guido Imbens.

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

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

4. 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.

5. 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.

Feature Selection in causal inference

1. Ultra-high dimensional variable selection for doubly robust causal inference, Biometrics, 2022. paper code slides

   Dingke Tang, Dehan Kong, Wenliang Pan, Linbo Wang

2. Outcome‐adaptive lasso: variable selection for causal inference, Biometrics 2017. paper video

   Susan M. Shortreed, Ashkan Ertefaie

Applications

Social sciences

1. Double machine learning-based programme evaluation under unconfoundedness, The Econometrics Journal, 2022. paper

   Michael C Knaus.

2. State-Building through Public Land Disposal? An Application of Matrix Completion for Counterfactual Prediction, arXiv, 2021. paper code

   Jason Poulos.

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

   Jason Poulos, Shuxi Zeng.

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

   Susan Athey, Stefan Wager.

5. 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.

Health

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 code

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

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

3. Machine Learning & Causal Inference: A Short Course link

4. KDD 2020: Lecture Style Tutorials: Casual Inference Meets Machine Learning 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

Books

1. Causal Machine Learning link

awesome-causality-algorithms

An index of algorithms in

• machine learning for causal inference: solves causal inference problems
• causal machine learning: solves ML problems Reproducibility is important! We will remove those methods without open-source code unless it is a survey/review paper. Please cite our survey paper if this index is helpful.

@article{guo2020survey,
  title={A survey of learning causality with data: Problems and methods},
  author={Guo, Ruocheng and Cheng, Lu and Li, Jundong and Hahn, P Richard and Liu, Huan},
  journal={ACM Computing Surveys (CSUR)},
  volume={53},
  number={4},
  pages={1--37},
  year={2020},
  publisher={ACM New York, NY, USA}

Table of Contents

• Toolboxes
• Causal Effect Estimation
• Causal Machine Learning

Toolboxes

Comprehensive

Name	Code	Comment
Trustworthy AI	Python	Causal Structure Learning, Causal Disentangled Representation Learning, gCastle (or pyCastle, pCastle).
YLearn	Python	Python package for causal discovery，causal effect identification/estimation, counterfactual inference，policy learning，etc.

Treatment Effect Estimation / Uplift Modeling

Name	Paper/Documentation	Venue	Code	Comment
DoWhy	Tutorial on Causal Inference and Counterfactual Reasoning	KDD 2018	Python	Python library for causal inference that supports explicit modeling and testing of causal assumptions.
EconML	Causal Inference and Machine Learning in Practice with EconML and CausalML	KDD 2021	Python	Python package that applies the power of machine learning techniques to estimate individualized causal responses from observational or experimental data.
CausalML	Causalml: Python package for causal machine learning	arxiv	Python	Uplift modeling and causal inference with machine learning algorithms
JustCause	Underlying thesis	NA	Python	For evaluation of heterogeneous treatment effect estimators on common reference as well as synthetic data.
WhyNot	Documentation	NA	Python	An experimental sandbox for causal inference and decision making in dynamics.
scikit-uplift	Documentation and User guide for uplift modeling	NA	Python	Uplift modeling in scikit-learn style in python.

Causal Discovery

Name	Paper	Code	Comment
Bench Press	Benchpress: a scalable and versatile workflow for benchmarking structure learning algorithms for graphical models	Code	Reproducible and scalable execution and benchmarks of 41 structure learning algorithms supporting multiple language
causal-learn	NA	Python	Causal Discovery for Python. A translation and extension of TETRAD.
TETRAD R/Java	TETRAD-A Toolbox FOR CAUSAL DISCOVERY	R/Java	Causal Discovery Toolbox from CMU
Causaldag	NA	code	Python package for the creation, manipulation, and learning of Causal DAGs
CausalNex	NA	Python	A toolkit for causal reasoning with Bayesian Networks.
CausalDiscoveryToolbox	Causal Discovery Toolbox: Uncover causal relationships in Python	Python

Rootcause Analysis

Name	Paper	Code	Comments
Chaos Genius	NA	Python	ML powered analytics engine for outlier/anomaly detection and root cause analysis.

Causal Effect Estimation

Survey Papers

Name	Paper	Venue
	A survey on causal inference	TKDD

With i.i.d Data

Individual Treatment Effect (ITE) / Conditional Average Treatment Effect (CATE)

Deep Learning based methods

Name	Paper	Venue	Code
TARNet, Counterfactual Regression	Estimating individual treatment effect: generalization bounds and algorithms	ICML 2017	Python
BNN, BLR	Learning representations for counterfactual inference	ICML 2016	Python
Causal Effect VAE	Causal effect inference with deep latent-variable models	Neurips 2017	Python
Dragonnet	Adapting neural networks for the estimation of treatment effects.	Neurips 2019	Python
SITE	Representation Learning for Treatment Effect Estimation from Observational Data	Neurips 2018	Python
GANITE	GANITE: Estimation of Individualized Treatment Effects using Generative Adversarial Nets	ICLR 2018	Python
Perfect Match	Perfect match: A simple method for learning representations for counterfactual inference with neural networks	arxiv	Python
Intact-VAE	Intact-VAE: Estimating treatment effects under unobserved confounding	ICLR 2022	code
CausalEGM	CausalEGM: a general causal inference framework by encoding generative modeling	arxiv	Python

Classic Methods

Name	Paper	Code
Propensity Score Matching	Rosenbaum, Paul R., and Donald B. Rubin. "The central role of the propensity score in observational studies for causal effects." Biometrika 70, no. 1 (1983): 41-55.	Python

Tree based Methods

Name	Paper	Code
Causal Forest	Wager, Stefan, and Susan Athey. "Estimation and inference of heterogeneous treatment effects using random forests." JASA (2017).	code R, code Python
Causal MARS, Causal Boosting, Pollinated Transformed Outcome Forests	S. Powers et al., “Some methods for heterogeneous treatment effect estimation in high-dimensions,” 2017.	code R, code R
Bayesian Additive Regression Trees (BART)	Hill, Jennifer L. "Bayesian nonparametric modeling for causal inference." Journal of Computational and Graphical Statistics 20, no. 1 (2011): 217-240.	Python

Meta Learner

Name	Paper	Code
X-learner	Künzel, Sören R., Jasjeet S. Sekhon, Peter J. Bickel, and Bin Yu. "Metalearners for estimating heterogeneous treatment effects using machine learning." Proceedings of the National Academy of Sciences 116, no. 10 (2019): 4156-4165.	code R, code R

Average Treatment Effect (including ATT and ATC)

Name	Paper	Code
Inverse Probability Reweighting	Rosenbaum, Paul R., and Donald B. Rubin. "The central role of the propensity score in observational studies for causal effects." Biometrika 70, no. 1 (1983): 41-55.	R
Doubly Robust Estimation	Bang, Heejung, and James M. Robins. "Doubly robust estimation in missing data and causal inference models." Biometrics 61, no. 4 (2005): 962-973.	R
Doubly Robust Estimation for High Dimensional Data	Antonelli, Joseph, Matthew Cefalu, Nathan Palmer, and Denis Agniel. "Doubly robust matching estimators for high dimensional confounding adjustment." Biometrics (2016).	R
TMLE	Gruber, Susan, and Mark J. van der Laan. "tmle: An R package for targeted maximum likelihood estimation." (2011).	R
Entropy Balancing	Hainmueller, Jens. "Entropy balancing for causal effects: A multivariate reweighting method to produce balanced samples in observational studies." Political Analysis 20, no. 1 (2012): 25-46.	R
CBPS(Covariate Balancing Propensity Score)	Imai, Kosuke, and Marc Ratkovic. "Covariate balancing propensity score." Journal of the Royal Statistical Society: Series B (Statistical Methodology) 76, no. 1 (2014): 243-263.	R
Approximate Residual Balancing	Athey, Susan, Guido W. Imbens, and Stefan Wager. "Approximate residual balancing: debiased inference of average treatment effects in high dimensions." Journal of the Royal Statistical Society: Series B (Statistical Methodology) 80, no. 4 (2018): 597-623.	R
Causal Bootstrapping	Little, Max A., and Reham Badawy. "Causal bootstrapping." arXiv preprint arXiv:1910.09648 (2019).	Matlab

Instrumental Variable (IV)

Name	Paper	Code
DeepIV	Hartford, Jason, Greg Lewis, Kevin Leyton-Brown, and Matt Taddy. "Deep iv: A flexible approach for counterfactual prediction." In International Conference on Machine Learning, pp. 1414-1423. 2017.	Python
PDSLasso	Achim Ahrens & Christian B. Hansen & Mark E Schaffer, 2018. "PDSLASSO: Stata module for post-selection and post-regularization OLS or IV estimation and inference," Statistical Software Components S458459, Boston College Department of Economics, revised 24 Jan 2019.	STATA

Does-Response Curve (Continuous Treatment)

Name	Paper	Code
Causal Dose-Response Curves / Causal Curves	Kobrosly, R. W., (2020). causal-curve: A Python Causal Inference Package to Estimate Causal Dose-Response Curves. Journal of Open Source Software, 5(52), 2523, https://doi.org/10.21105/joss.02523	Python
Dose response networks (DRNets)	Schwab, Patrick, Lorenz Linhardt, Stefan Bauer, Joachim M. Buhmann, and Walter Karlen. "Learning Counterfactual Representations for Estimating Individual Dose-Response Curves." arXiv preprint arXiv:1902.00981 (2019).	Python

Vectorized Treatments

Name	Paper	Code
Causal Effect Inference for Structured Treatments	Jean Kaddour, Qi Liu, Yuchen Zhu, Matt J. Kusner, Ricardo Silva. "Causal Effect Inference for Structured Treatments", In NeurIPS 2021.	Python

Multiple Causes

Name	Paper	Code
Deconfounder	Wang, Yixin, and David M. Blei. "The blessings of multiple causes." arXiv preprint arXiv:1805.06826 (2018).	Python

Multiple Outcomes

Name	Paper	Code
Multiple Responses in Uplift Models	Weiss, Sam. Estimating and Visualizing Business Tradeoffs in Uplift Models	Python

Non-i.i.d Data

Panel Data / Time Series

Name	Paper	Code
Synthetic Control Method	Abadie, Alberto. "Using synthetic controls: Feasibility, data requirements, and methodological aspects." Journal of Economic Literature 59.2 (2021): 391-425.	R
Synthetic Difference in Differences	Arkhangelsky, Dmitry, et al. Synthetic difference in differences. No. w25532. National Bureau of Economic Research, 2019.	R Python
Causal Impact	Brodersen, K. H., Gallusser, F., Koehler, J., Remy, N., & Scott, S. L. (2015). Inferring causal impact using Bayesian structural time-series models. The Annals of Applied Statistics, 9(1), 247–274. doi: 10.1214/14-AOAS788	R Python
Time Series Deconfounder	Bica, Ioana, Ahmed M. Alaa, and Mihaela van der Schaar. "Time Series Deconfounder: Estimating Treatment Effects over Time in the Presence of Hidden Confounders." In ICML 2020.	code
Recurrent Marginal Structural Networks	Lim, Bryan. "Forecasting Treatment Responses Over Time Using Recurrent Marginal Structural Networks." In Advances in Neural Information Processing Systems, pp. 7494-7504. 2018.	Python
Longitudinal Targeted Maximum Likelihood Estimation	Petersen, Maya, Joshua Schwab, Susan Gruber, Nello Blaser, Michael Schomaker, and Mark van der Laan. "Targeted maximum likelihood estimation for dynamic and static longitudinal marginal structural working models." Journal of causal inference 2, no. 2 (2014): 147-185.	R
Causal Transformer	Melnychuk, Valentyn, Dennis Frauen, and Stefan Feuerriegel. "Causal Transformer for Estimating Counterfactual Outcomes." arXiv preprint arXiv:2204.07258 (2022).	Python

Network Data (with or without Interference)

Name	Paper	Code
Network Deconfounder	Guo, Ruocheng, Jundong Li, and Huan Liu. "Learning Individual Causal Effects from Networked Observational Data." WSDM 2020.	Python
Causal Inference with Network Embeddings	Veitch, Victor, Yixin Wang, and David M. Blei. "Using embeddings to correct for unobserved confounding." arXiv preprint arXiv:1902.04114 (2019).	Python
Linked Causal Variational Autoencoder (LCVA)	Rakesh, Vineeth, Ruocheng Guo, Raha Moraffah, Nitin Agarwal, and Huan Liu. "Linked Causal Variational Autoencoder for Inferring Paired Spillover Effects." CIKM 2018.	Python
Method-of-moments Estimators	Li, Wenrui, Daniel L. Sussman, and Eric D. Kolaczyk. "Causal Inference under Network Interference with Noise." arXiv preprint arXiv:2105.04518 (2021).	code

Causal Machine Learning

Surveys

Name	Paper	Code
CausalML	Jean Kaddour, Aengus Lynch, Qi Liu, Matt J. Kusner, Ricardo Silva. "Causal Machine Learning: A Survey and Open Problems" arXiv preprint arXiv:2206.15475 (2022).	NA

OoD Generalization

Name	Paper	Code
DomainBed	Gulrajani, Ishaan, and David Lopez-Paz. "In Search of Lost Domain Generalization." In International Conference on Learning Representations. 2020.	code
WILDS	Koh, Pang Wei, Shiori Sagawa, Henrik Marklund, Sang Michael Xie, Marvin Zhang, Akshay Balsubramani, Weihua Hu et al. "Wilds: A benchmark of in-the-wild distribution shifts." In International Conference on Machine Learning, pp. 5637-5664. PMLR, 2021.	code
IBM OoD	Repository for theory and methods for Out-of-Distribution (OoD) generalization by IBM Research	code
OoD Bench	Ye, Nanyang, Kaican Li, Lanqing Hong, Haoyue Bai, Yiting Chen, Fengwei Zhou, and Zhenguo Li. "Ood-bench: Benchmarking and understanding out-of-distribution generalization datasets and algorithms." arXiv preprint arXiv:2106.03721 (2021).	code
BEDS-Bench	Avati, Anand, Martin Seneviratne, Emily Xue, Zhen Xu, Balaji Lakshminarayanan, and Andrew M. Dai. "BEDS-Bench: Behavior of EHR-models under Distributional Shift--A Benchmark." arXiv preprint arXiv:2107.08189 (2021).	code
Survey THU	Shen, Zheyan, Jiashuo Liu, Yue He, Xingxuan Zhang, Renzhe Xu, Han Yu, and Peng Cui. "Towards out-of-distribution generalization: A survey." arXiv preprint arXiv:2108.13624 (2021).	NA

Graph OoD Generalization

Name	Paper	Code
CIGA	Chen, Yongqiang, Yonggang Zhang, Yatao Bian, Han Yang, Kaili Ma, Binghui Xie, Tongliang Liu, Bo Han, and James Cheng. "Learning Causally Invariant Representations for Out-of-Distribution Generalization on Graphs." In Advances in Neural Information Processing Systems (2022).	code
Survey THU	Li, Haoyang, Xin Wang, Ziwei Zhang, and Wenwu Zhu. "Out-of-distribution generalization on graphs: A survey." arXiv preprint arXiv:2202.07987 (2022).	NA

Recommendation Systems

Inverse Propensity Scoring / Doubly Robust

Name	Paper	Code
Top-K Off-policy Correction	Chen, Minmin, Alex Beutel, Paul Covington, Sagar Jain, Francois Belletti, and Ed H. Chi. "Top-k off-policy correction for a REINFORCE recommender system." In Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining, pp. 456-464. ACM, 2019.	Python
Unbiased Offline Recommender Learning	Saito, Yuta, Suguru Yaginuma, Yuta Nishino, Hayato Sakata, and Kazuhide Nakata. "Unbiased Recommender Learning from Missing-Not-At-Random Implicit Feedback." In Proceedings of the 13th International Conference on Web Search and Data Mining, pp. 501-509. ACM, 2020.	Python
Unbiased Offline Recommender Evaluation	Yang, Longqi, Yin Cui, Yuan Xuan, Chenyang Wang, Serge Belongie, and Deborah Estrin. "Unbiased offline recommender evaluation for missing-not-at-random implicit feedback." In Proceedings of the 12th ACM Conference on Recommender Systems, pp. 279-287. ACM, 2018.	Python
IPS Estimator	Schnabel, Tobias, Adith Swaminathan, Ashudeep Singh, Navin Chandak, and Thorsten Joachims. "Recommendations as treatments: Debiasing learning and evaluation." arXiv preprint arXiv:1602.05352 (2016).	Python

Hidden Confounding

Name	Paper	Code
Deconfounded Recsys	Wang, Yixin, Dawen Liang, Laurent Charlin, and David M. Blei. "Causal Inference for Recommender Systems." In Proceedings of the Fourteenth ACM Conference on Recommender Systems (2020).	Python

Domain Adaptation

Name	Paper	Code
Causal Embedding for Recommendation	Bonner, Stephen, and Flavian Vasile. "Causal embeddings for recommendation." In Proceedings of the 12th ACM Conference on Recommender Systems, pp. 104-112. ACM, 2018. (BEST PAPER)	Python
Domain Adversarial Matrix Factorization	Saito, Yuta, and Masahiro Nomura. "Towards Resolving Propensity Contradiction in Offline Recommender Learning." In IJCAI 2022	code

Disentanglement

Name	Paper	Code
Causal Embedding for User Interest and Conformity	Zheng, Y., Gao, C., Li, X., He, X., Li, Y., & Jin, D. (2021, April). Disentangling User Interest and Conformity for Recommendation with Causal Embedding. In Proceedings of the Web Conference 2021 (pp. 2980-2991).	Python

Learning to Rank

Name	Paper	Code
Policy-aware Estimator	Oosterhuis, Harrie, and Maarten de Rijke. "Policy-aware unbiased learning to rank for top-k rankings." In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 489-498. 2020.	Python
Heckman^{rank}	Ovaisi, Zohreh, Ragib Ahsan, Yifan Zhang, Kathryn Vasilaky, and Elena Zheleva. "Correcting for Selection Bias in Learning-to-rank Systems." arXiv preprint arXiv:2001.11358 (2020).	code
Unbiased LambdaMart	Hu, Ziniu, Yang Wang, Qu Peng, and Hang Li. "Unbiased LambdaMART: An Unbiased Pairwise Learning-to-Rank Algorithm." In The World Wide Web Conference, pp. 2830-2836. ACM, 2019.	code
IPW_rank and the Dual Learning Algorithm	Qingyao Ai, Keping Bi, Cheng Luo, Jiafeng Guo, W. Bruce Croft. 2018. Unbiased Learning to Rank with Unbiased Propensity Estimation. In Proceedings of SIGIR '18	Python
Propensity SVM-rank	Joachims, Thorsten, Adith Swaminathan, and Tobias Schnabel. "Unbiased learning-to-rank with biased feedback." In Proceedings of the Tenth ACM International Conference on Web Search and Data Mining, pp. 781-789. ACM, 2017. (BEST PAPER)	Python

Off-line Policy Evaluation/Optimization (for Contextual Bandit or RL)

Name	Paper	Code
Optimal Kernel Balancing	Andrew Bennett, Nathan Kallus. "Policy Evaluation with Latent Confounders via Optimal Balance"	Python
BanditNet	Joachims, Thorsten, Adith Swaminathan, and Maarten de Rijke. "Deep learning with logged bandit feedback." (2018).	Python
Counterfactual Risk Minimization (POEM)	Swaminathan, Adith, and Thorsten Joachims. "Counterfactual risk minimization: Learning from logged bandit feedback." In International Conference on Machine Learning, pp. 814-823. 2015.	Python
Self Normalized Estimator	Swaminathan, Adith, and Thorsten Joachims. "The self-normalized estimator for counterfactual learning." In Advances in Neural Information Processing Systems, pp. 3231-3239. 2015.	Python

Natural Language Processing

Name	Paper	Code
A Review of Using Text to Remove Confounding from Causal Estimates	Keith, Katherine A., David Jensen, and Brendan O'Connor. "Text and Causal Inference: A Review of Using Text to Remove Confounding from Causal Estimates." ACL 2020.	NA
Causal Analysis with Lexicons	Pryzant, Reid, Kelly Shen, Dan Jurafsky, and Stefan Wagner. "Deconfounded lexicon induction for interpretable social science." NAACL 2018.	Python
Causal Text Embeddings	Veitch, Victor, Dhanya Sridhar, and David M. Blei. "Using Text Embeddings for Causal Inference." arXiv preprint arXiv:1905.12741 (2019).	Python
Handling Missing/Noisy Treatment	Wood-Doughty, Zach, Ilya Shpitser, and Mark Dredze. "Challenges of Using Text Classifiers for Causal Inference." In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp. 4586-4598. 2018.	Python
Causal Inferences Using Texts	Egami, Naoki, Christian J. Fong, Justin Grimmer, Margaret E. Roberts, and Brandon M. Stewart. "How to make causal inferences using texts." arXiv preprint arXiv:1802.02163 (2018).	NA

Counterfactual Explanations

Paper	Code
Mothilal, Ramaravind Kommiya, Amit Sharma, and Chenhao Tan. "Explaining machine learning classifiers through diverse counterfactual explanations." arXiv preprint arXiv:1905.07697 (2019).	Python
Russell, Chris. "Efficient search for diverse coherent explanations." In Proceedings of the Conference on Fairness, Accountability, and Transparency, pp. 20-28. 2019.	Python
Wachter, Sandra, Brent Mittelstadt, and Chris Russell. "Counterfactual explanations without opening the black box: Automated decisions and the GDPR." Harv. JL & Tech. 31 (2017): 841.

Counterfactual Fairness

Paper	Code
Kusner, Matt J., Joshua Loftus, Chris Russell, and Ricardo Silva. "Counterfactual fairness." In Advances in Neural Information Processing Systems, pp. 4066-4076. 2017.	Python
Wu, Yongkai, Lu Zhang, Xintao Wu, and Hanghang Tong. "Pc-fairness: A unified framework for measuring causality-based fairness." Advances in Neural Information Processing Systems 32 (2019).	code
Chiappa, Silvia. "Path-specific counterfactual fairness." In Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 7801-7808. 2019.	code
Garg, Sahaj, Vincent Perot, Nicole Limtiaco, Ankur Taly, Ed H. Chi, and Alex Beutel. "Counterfactual fairness in text classification through robustness." In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society, pp. 219-226. 2019.	code

Reinforcement Learning

Name	Paper	Code
Deconfounded RL	Lu, Chaochao, Bernhard Schölkopf, and José Miguel Hernández-Lobato. "Deconfounding reinforcement learning in observational settings." arXiv preprint arXiv:1812.10576 (2018).	Python
	Vansteelandt, Stijn, and Marshall Joffe. "Structural nested models and G-estimation: the partially realized promise." Statistical Science 29, no. 4 (2014): 707-731.	NA
Counterfactual-Guided Policy Search (CF-GPS)	Buesing, Lars, Theophane Weber, Yori Zwols, Sebastien Racaniere, Arthur Guez, Jean-Baptiste Lespiau, and Nicolas Heess. "Woulda, Coulda, Shoulda: Counterfactually-Guided Policy Search." arXiv preprint arXiv:1811.06272 (2018).	NA

Multi-Armed Bandit/Causal Bandit

Name	Paper	Code
Causal Bandits	Lattimore, Finnian, Tor Lattimore, and Mark D. Reid. "Causal bandits: Learning good interventions via causal inference." In Advances in Neural Information Processing Systems, pp. 1181-1189. 2016.	NA
Offline+MAB	Ye, Li, Yishi Lin, Hong Xie, and John Lui. "Combining Offline Causal Inference and Online Bandit Learning for Data Driven Decisions." arXiv preprint arXiv:2001.05699 (2020).	NA
B-kl-UCB, B-TS	Zhang, Junzhe, and Elias Bareinboim. "Transfer learning in multi-armed bandit: a causal approach." In Proceedings of the 16th Conference on Autonomous Agents and MultiAgent Systems, pp. 1778-1780. 2017.	NA
Incremental Model	Sawant, Neela, Chitti Babu Namballa, Narayanan Sadagopan, and Houssam Nassif. "Contextual Multi-Armed Bandits for Causal Marketing." arXiv preprint arXiv:1810.01859 (2018).	NA

Causal Discovery

for i.i.d. Data

Classic Methods

Name	Paper	Code
IC algorithm		Python
PC algorithm	P. Spirtes, C. Glymour, and R. Scheines. Causation, Prediction, and Search. The MIT Press, 2nd edition, 2000.	Python R Julia
FCI algorithm	P. Spirtes, C. Glymour, and R. Scheines. Causation, Prediction, and Search. The MIT Press, 2nd edition, 2000.	R Julia

Continuous Optimization

Paper	Venue	Code
DAGs with NO TEARS: Continuous optimization for structure learning	NeurIPS 2018	code
DAG-GNN: DAG Structure Learning with Graph Neural Networks	ICML 2019	code
Learning Sparse Nonparametric DAGs	AISTATS 2020	code
Amortized Inference for Causal Structure Learning	Neurips 2022	code

Learning IV

Name	Paper	Code
Learning instrumental variables with structural and non-gaussianity assumptions	JMLR	code

Distinguishing Cause from Effect (Bivariate)

Name	Paper	Code
BMLiNGAM	S. Shimizu and K. Bollen. Bayesian estimation of causal direction in acyclic structural equation models with individual-specific confounder variables and non-Gaussian distributions. Journal of Machine Learning Research, 15: 2629-2652, 2014.	Python
Sloppy	Marx, A & Vreeken, J Identifiability of Cause and Effect using Regularized Regression. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), ACM, 2019.	R
RECI	Blöbaum, Patrick, Dominik Janzing, Takashi Washio, Shohei Shimizu, and Bernhard Schölkopf. "Cause-effect inference by comparing regression errors." In International Conference on Artificial Intelligence and Statistics, pp. 900-909. PMLR, 2018.	in CausalDiscoveryToolbox
bQCD	Tagasovska, Natasa, Valérie Chavez-Demoulin, and Thibault Vatter. "Distinguishing cause from effect using quantiles: Bivariate quantile causal discovery." In International Conference on Machine Learning, pp. 9311-9323. PMLR, 2020.	code
CGNN	Goudet, Olivier, Diviyan Kalainathan, Philippe Caillou, Isabelle Guyon, David Lopez-Paz, and Michele Sebag. "Learning functional causal models with generative neural networks." In Explainable and interpretable models in computer vision and machine learning, pp. 39-80. Springer, Cham, 2018.	code

Conditional Independence Tests (for Constraint-based Algorithms)

Name	Paper	Code
RCIT		R

Causal Discovery with Probabilistic Logic Programming

Name	Paper	Code
Causal PSL	Sridhar, Dhanya, Jay Pujara, and Lise Getoor. "Scalable Probabilistic Causal Structure Discovery." In IJCAI, pp. 5112-5118. 2018.	Java

Scalable Ensemble Causal Discovery

Name	Paper	Code
Scalable and Hybrid Ensemble-Based Causality Discovery	Pei Guo, Achuna Ofonedu, Jianwu Wang. "Scalable and Hybrid Ensemble-Based Causality Discovery." In Proceedings of the 2020 IEEE International Conference on Smart Data Services (SMDS), pp. 72-80.	Python

with Temporal Data

Name	Paper	Code
TCDF: Temporal Causal Discovery Framework	Nauta, Meike, Doina Bucur, and Christin Seifert. "Causal discovery with attention-based convolutional neural networks." Machine Learning and Knowledge Extraction.	Pytorch

Awesome Causality Links

• Awesome Causality
  • Other Awesome lists
• Data
• Tools
• Learning resources
  • Tutorials
  • Blogs
  • Books
  • Courses
  • Videos
• Events
  • Workshops
• Communities, and Mailing lists
• Miscellaneous Table of contents generated with markdown-toc

Other Awesome lists

These list contain a more focused compilation of algorithms and data related to causality under more specific categories.

• awesome-causality-algorithms
• awesome-causality-data

Data

• Amazon Review Sales - Google drive - Paper
• Jobs Training - Train Test - Paper
• Twins
• Synthetic IHDP
• 2016 Atlantic Causal Inference competition
• News trearment effect measurement
• Cause effect pairs
• Movie recommendations - Missing not at random (MNAR) - Paper
• CHALEARN Fast Causation Coefficient Challenge - Kaggle
• Causal inference datasets in quantitative social sciences

Tools

• Counter factual regression
• DoWhy - Microsoft Research
• Quantitative Social Science - Book
• Causal Inference using Bayesian Additive Regression Trees
• Non-parametrics for Causal Inference
• Causality by author of Causal Data Science Series (see blogs)
• InvariantCausalPrediction: Invariant Causal Prediction
• Causal Discovery Toolbox
• CausalImpact - causal inference in time series
• Daggity - Create causal graphs
• TETRAD
• ProbLog - Do-calculus
• Causalnex - A toolkit for causal reasoning with Bayesian Networks
• Causal Fusion - A web based app for drawing and making inference via do-calculus on causal diagrams

Learning resources

Tutorials

• CompSci 590.6, Understanding Data: Theory and Applications Lecture 15 Causality in AI Instructor: Sudeepa Roy Email: [email protected]
• ICML 2016 Tutorial Causal Inference for Observational Studies
• KDD 2018 Causal Inference Tutorial
• Joris Mooij ML2 Causality
• Emre Kiciman - Observational Studies in Social Media (OSSM) at ICWSM 2017
• The Blessings of Multiple Causes: A Tutorial
• Susan Athey: Counterfactual Inference (NeurIPS 2018 Tutorial) - Slides
• Ferenc Huszár Causal Inference Practical from MLSS Africa 2019 - [Notebook Runthrough] [Video 1] [Video 2]
• Causality notes and implementation in Python using statsmodels and networkX
• Thinking Clearly About Correlations and Causation: Graphical Causal Models for Observational Data
• The Hitchhiker’s Guide to the tlverse or a Targeted Learning Practitioner’s Handbook

Blogs, and Articles

• Causal Data Science Series
• Ferenc Huszár Series on Causal Modelling: various parts - 1, 2, 3, 4
• Diving deeper into causality Pearl, Kleinberg, Hill and untested assumptions
• Simpson's Paradox: An Anatomy
• Simpson’s paradox and causal inference with observational data
• Causation and Correlation - Talks about possible causes for observed correlations
• (Non-)Identification in Latent Confounder Models
• Causal Inference Animated Plots - Good explanation of various causal inference methods
• Explanation, prediction, and causality: Three sides of the same coin?
• A chill intro to causal inference via propensity scores
• All the DAGs from Hernan and Robins' Causal Inference Book by Sam Finlayson - Causal Inference Book Part I -- Glossary and Notes
• Causal Inference with Bayes Rule by Gradient Institute
• Causal Inference cheat sheet for data scientists

Books

• Causal Inference Book
• Causal Inference in statistics: A primer
• Elements of Causal Inference - Foundations and Learning Algorithms (includes code examples in R and Jupyter notebooks)
• The Book of Why: The New Science of Cause and Effect
• Causal Inference Mixtape
• Elements of Causal Inference - Foundations and Learning Algorithms
• Actual Causality By Joseph Y. Halpern
• Causal Reasoning: Fundamentals and Machine Learning Applications by Emre Kiciman and Amit Sharma

Courses

• Causal Diagrams: Draw Your Assumptions Before Your Conclusions
• Causal Inference: prediction, explanation, and intervention
• Causal Inference Experiments Short Course
• ECON 305: Economics, Causality, and Analytics [github]
• Algorithmic Information Dynamics: A Computational Approach to Causality and Living Systems From Networks to Cells
• Four Lectures on Causality by Jonas Peters
• Julian Schuessler's Causal Graphs Seminar - Winner of 2019 American Statistics Association Causality in Statistics Education Award
• Ilya Shpitser's course on Causal Inference (Zip file) - Winner of 2017 American Statistics Association Causality in Statistics Education Award
• Arvid Sjölander's course on Causal Inference (Zip file) - Winner of 2016 American Statistics Association Causality in Statistics Education Award
• Onyebuchi A. Arah course on Causality in Statistics (Dropbox folder) - Winner of 2016 American Statistics Association Causality in Statistics Education Award
• Introduction to causal inference by Maya L. Petersen & Laura B. Balzer

Videos

• PyData LA 2018 Keynote: Judea Pearl - The New Science of Cause and Effect
• CACM Mar. 2019 - The Seven Tools of Causal Inference
• ACM Turing Award Lecture 2011 - Judea Pearl
• Leon Bottou - Learning representations using causal invariance

Events

Workshops

• Beyond Curve Fitting: Causation, Counterfactuals, and Imagination-based AI
• Causality Challenge #1: Causation and Prediction
• NIPS 2013 Workshop on Causality
• ChaLearn Fast Causation Coefficient Challenge

Communities, and Mailing lists

• Causality Challenge Google group

Miscellaneous

• Causal Inference Reading list
• Causal inference paper reading list
• American Statistics Association Causality in Statistics Education Award