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Many methods have been proposed to estimate treatment effects with observational data.
Randomization analysis of experimental data: The fisher randomization test comment
Donald B Rubin · 1980
Earlier work this paper cites.
The central role of the propensity score in observational studies for causal effects
Paul R Rosenbaum and Donald B Rubin · 1983
Earlier work this paper cites.
Causal diagrams for empirical research
Judea Pearl · 1995
Earlier work this paper cites.
The mnist database of handwritten digits
Yann LeCun · 1998
Earlier work this paper cites.
An introduction to the augmented inverse propensity weighted estimator
Adam N Glynn and Kevin M Quinn · 2010
Earlier work this paper cites.
Bayesian nonparametric modeling for causal inference
Jennifer L Hill · 2011
Earlier work this paper cites.
Transportability of causal and statistical relations: A formal approach
Judea Pearl and Elias Bareinboim · 2011
Earlier work this paper cites.
Transportability from multiple environments with limited experiments: Completeness results
Elias Bareinboim and Judea Pearl · 2014
Earlier work this paper cites.
Causal effect inference with deep latent-variable models
Christos Louizos, Uri Shalit, Joris M Mooij, David Sontag, Richard Zemel, and Max Welling · 2017
Earlier work this paper cites.
Estimating individual treatment effect: generalization bounds and algorithms
Uri Shalit, Fredrik D Johansson, and David Sontag · 2017
Cited alongside, same era.
Metalearners for estimating heterogeneous treatment effects using machine learning
Sören R Künzel, Jasjeet S Sekhon, Peter J Bickel, and Bin Yu · 2019
Cited alongside, same era.
Adapting neural networks for the estimation of treatment effects
Claudia Shi, David Blei, and Victor Veitch · 2019
Cited alongside, same era.
The blessings of multiple causes
Yixin Wang and David M Blei · 2019
Cited alongside, same era.
Combining multiple observational data sources to estimate causal effects
Shu Yang and Peng Ding · 2019
Cited alongside, same era.
Identifying causal-effect inference failure with uncertainty-aware models
Andrew Jesson, Sören Mindermann, Uri Shalit, and Yarin Gal · 2020
A comprehensive survey on transfer learning
Fuzhen Zhuang, Zhiyuan Qi, Keyu Duan, Dongbo Xi, Yongchun Zhu, Hengshu Zhu, Hui Xiong, and Qing He · 2020
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Parkca: Causal inference with partially known causes
Raquel Aoki and Martin Ester · 2021
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BayesIMP: Uncertainty quantification for causal data fusion
Siu Lun Chau, Jean-Francois Ton, Javier Gonzalez, Yee Whye Teh, and Dino Sejdinovic · 2021
Later among the works it cites.
Multi-source causal inference using control variates
Wenshuo Guo, Serena Wang, Peng Ding, Yixin Wang, and Michael I Jordan · 2021
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Quantifying ignorance in individual-level causal-effect estimates under hidden confounding
Andrew Jesson, Sören Mindermann, Yarin Gal, and Uri Shalit · 2021
Later among the works it cites.
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Cited alongside, same era.
An introduction to proximal causal learning
Eric J Tchetgen Tchetgen, Andrew Ying, Yifan Cui, Xu Shi, and Wang Miao · 2020
Cited alongside, same era.
Sense and sensitivity analysis: Simple post-hoc analysis of bias due to unobserved confounding
Victor Veitch and Anisha Zaveri · 2020
Cited alongside, same era.
Afsaneh Mastouri, Yuchen Zhu, Limor Gultchin, Anna Korba, Ricardo Silva, Matt J Kusner, Arthur Gretton, and Krikamol Muandet · 2021
Later among the works it cites.
A critical look at the consistency of causal estimation with deep latent variable models
Severi Rissanen and Pekka Marttinen · 2021
Later among the works it cites.
Copula-based sensitivity analysis for multi-treatment causal inference with unobserved confounding
Jiajing Zheng, Alexander D’Amour, and Alexander Franks · 2021
Later among the works it cites.