/awesome-causal-learning

Causality with machine learning, topic including causal represenation learning, causal reinforcement learning

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awesome-causal-learning Awesome

Research combines causal inference with machine learning, topic includes CausalML, Causal Representation learning, Causal Reinforcement Learning with its applicaiton in NLP and CV.

Table of Contents

causal machine learning

From IID data

Individual Treatment Effects (ITE)

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
Nonparametric Regression Adjustment Python
BNN, BLR Johansson, Fredrik, Uri Shalit, and David Sontag. "Learning representations for counterfactual inference." 33rd International Conference on Machine Learning (ICML), June 2016.
TARNet, CFR Shalit, Uri, Fredrik D. Johansson, and David Sontag. "Estimating individual treatment effect: generalization bounds and algorithms." 34th International Conference on Machine Learning (ICML), August 2017. Python
CEVAE Louizos, Christos, Uri Shalit, Joris M. Mooij, David Sontag, Richard Zemel, and Max Welling. "Causal effect inference with deep latent-variable models." In Advances in Neural Information Processing Systems, pp. 6446-6456. 2017. Python
SITE Yao, Liuyi, Sheng Li, Yaliang Li, Mengdi Huai, Jing Gao, and Aidong Zhang. "Representation Learning for Treatment Effect Estimation from Observational Data." In Advances in Neural Information Processing Systems, pp. 2638-2648. 2018. Python
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. RR
Causal Forest Wager, Stefan, and Susan Athey. "Estimation and inference of heterogeneous treatment effects using random forests." Journal of the American Statistical Association just-accepted (2017). R Python
Causal MARS, Causal Boosting, Pollinated Transformed Outcome Forests S. Powers et al., “Some methods for heterogeneous treatment effect estimation in high-dimensions,” 2017. R R
BART Hill, Jennifer L. "Bayesian nonparametric modeling for causal inference." Journal of Computational and Graphical Statistics 20, no. 1 (2011): 217-240. Python
GANITE Yoon, Jinsung, James Jordon, and Mihaela van der Schaar. "GANITE: Estimation of Individualized Treatment Effects using Generative Adversarial Nets." (2018). Python
Perfect Match Schwab, Patrick, Lorenz Linhardt, and Walter Karlen. "Perfect match: A simple method for learning representations for counterfactual inference with neural networks." arXiv preprint arXiv:1810.00656 (2018) Python
Active Learning for Decision-Making from Imbalanced Observational Data Active Learning for Decision-Making from Imbalanced Observational Data NA
ABCEI Adversarial Balancing-based Representation Learning for Causal Effect Inference with Observational Data NA
NSGP (Non-stationary Gaussian Process Prior) Alaa, Ahmed, and Mihaela Schaar. "Limits of estimating heterogeneous treatment effects: Guidelines for practical algorithm design." In International Conference on Machine Learning, pp. 129-138. 2018. NA
CMGP (Causal Multi-task Gaussian Processes) Alaa, Ahmed M., and Mihaela van der Schaar. "Bayesian inference of individualized treatment effects using multi-task gaussian processes." In Advances in Neural Information Processing Systems, pp. 3424-3432. 2017. NA
BNR-NNM(balanced and nonlinear representations-nearest neighbor matching) Li, Sheng, and Yun Fu. "Matching on balanced nonlinear representations for treatment effects estimation." In Advances in Neural Information Processing Systems, pp. 929-939. 2017. NA
Deep Counterfactual Networks (Propensity Dropout) Alaa, Ahmed M., Michael Weisz, and Mihaela van der Schaar. "Deep counterfactual networks with propensity-dropout." arXiv preprint arXiv:1706.05966 (2017) NA
interval estimation(sensitivity analysis) Kallus, Nathan, Xiaojie Mao, and Angela Zhou. "Interval Estimation of Individual-Level Causal Effects Under Unobserved Confounding." In The 22nd International Conference on Artificial Intelligence and Statistics, pp. 2281-2290. 2019. NA

Averaged Treatment Effects (ATE)

Name Paper Code
Dragonnet Adapting Neural Networks for the Estimation of Treatment Effects Python
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
Differentiated Confounder Balancing Kuang, Kun, Peng Cui, Bo Li, Meng Jiang, and Shiqiang Yang. "Estimating Treatment Effect in the Wild via Differentiated Confounder Balancing." In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 265-274. ACM, 2017. NA
Adversarial Balancing Ozery-Flato, Michal, Pierre Thodoroff, and Tal El-Hay. "Adversarial Balancing for Causal Inference." arXiv preprint arXiv:1810.07406 (2018). NA
DeepMatch Kallus, Nathan. "Deepmatch: Balancing deep covariate representations for causal inference using adversarial training." arXiv preprint arXiv:1802.05664 (2018).ICML20 NA

Continuous Treatment Effects

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
RespSVM Kallus, Nathan. "Classifying Treatment Responders Under Causal Effect Monotonicity." ICML2019 NA
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." AAAI2020. Python

Instrumental Variables

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

Multi-Cause: An important discussion

Name Paper Code
Deconfounder Wang, Yixin, and David M. Blei. "The blessings of multiple causes." Journal of the American Statistical Association, 2019 Python
Imai, Kosuke, and Zhichao Jiang. "Discussion of "The Blessings of Multiple Causes" by Wang and Blei." NA
D'Amour, Alexander. "On multi-cause causal inference with unobserved confounding: Counterexamples, impossibility, and alternatives." arXiv preprint arXiv:1902.10286 (2019). NA
Ranganath, Rajesh, and Adler Perotte. "Multiple causal inference with latent confounding." arXiv preprint arXiv:1805.08273 (2018). NA
Kong, Dehan, Shu Yang, and Linbo Wang. "Multi-cause causal inference with unmeasured confounding and binary outcome." arXiv preprint arXiv:1907.13323 (2019). NA
Elizabeth L. Ogburn, Ilya Shpitser, Eric J. Tchetgen Tchetgen "Comment on Blessings of Multiple Causes." arXiv preprint arXiv:1910.05438 (2019) NA

From Non-IID data

social network

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

Spillover Effect/Interference

Name Paper Code
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
GNN-based Causal Effect Estimators Ma, Yunpu, Yuyi Wang, and Volker Tresp. "Causal Inference under Networked Interference." arXiv preprint arXiv:2002.08506 (2020). NA

Time Varying/Dependent Causal Effects

Name Paper Code
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." arXiv preprint arXiv:1902.00450 (2019). NA
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

Application in ML

Recommendation

NLP

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
Conditional Treatment-adversarial Learning Based Matching Yao, Liuyi, Sheng Li, Yaliang Li, Hongfei Xue, Jing Gao, and Aidong Zhang. "On the estimation of treatment effect with text covariates." In Proceedings of the 28th International Joint Conference on Artificial Intelligence, pp. 4106-4113. AAAI Press, 2019. NA
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

CV: debias

Name Paper Code J./C.
Class-Incremental Learning Distilling Causal Effect of Data in Class-Incremental Learning Python CVPR21
Counterfactual VQA Counterfactual VQA: A Cause-Effect Look at Language Bias Python CVPR21
Deconfounded Image Captioning Causal Attention for Vision-Language Tasks Python CVPR21
Weakly-supervised Temporal Action Localization The Blessings of Unlabeled Background in Untrimmed Videos Python CVPR21
Visual Dialog Two Causal Principles for Improving Visual Dialog Python CVPR20
Visual Common Sense Visual Commonsense R-CNN Python CVPR20
Scene Graph Generation Unbiased Scene Graph Generation from Biased Training Python CVPR21,oral
Interventional Few-Shot Learning Interventional Few-Shot Learning Python NeurIPS20

causal representation learning

Name Paper Code
Toward causal representation learning NA

causal reinforcement learning

Name Paper Code
Causal Reinforcement Learning NA