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Causal inference has numerous real-world applications in many domains, such as health care, marketing, political science, and online advertising.
Estimating causal effects of treatments in randomized and nonrandomized studies
Donald B Rubin · 1974
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The central role of the propensity score in observational studies for causal effects
Paul R Rosenbaum and Donald B Rubin · 1983
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Judea Pearl · 2000
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Shai Ben-David, John Blitzer, Koby Crammer, and Fernando Pereira · 2007
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Invited commentary: variable selection versus shrinkage in the control of multiple confounders
Sander Greenland · 2008
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Causal inference in statistics: An overview
Judea Pearl · 2009
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Causality
Judea Pearl · 2009
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Overadjustment bias and unnecessary adjustment in epidemiologic studies
Enrique F Schisterman, Stephen R Cole, and Robert W Platt · 2009
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Matching methods for causal inference: A review and a look forward
Elizabeth A Stuart · 2010
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Effects of adjusting for instrumental variables on bias and precision of effect estimates
Jessica A Myers, Jeremy A Rassen, Joshua J Gagne, Krista F Huybrechts, Sebastian Schneeweiss, Kenneth J Rothman, Marshall M Joffe, and Robert J Glynn · 2011
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On the hardness of domain adaptation and the utility of unlabeled target samples
Shai Ben-David and Ruth Urner · 2012
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A tutorial on propensity score estimation for multiple treatments using generalized boosted models
Daniel F McCaffrey, Beth Ann Griffin, Daniel Almirall, Mary Ellen Slaughter, Rajeev Ramchand, and Lane F Burgette · 2013
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A review of covariate selection for non-experimental comparative effectiveness research
Brian C Sauer, M Alan Brookhart, Jason Roy, and Tyler VanderWeele · 2013
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Bibhas Chakraborty and Susan A Murphy · 2014
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Confounder selection via penalized credible regions
Ander Wilson and Brian J Reich · 2014
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Uncertainty in propensity score estimation: Bayesian methods for variable selection and model-averaged causal effects
Corwin Matthew Zigler and Francesca Dominici · 2014
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Machine learning methods for estimating heterogeneous causal effects
Susan Athey and Guido W Imbens · 2015
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Causal inference in statistics, social, and biomedical sciences
Guido W Imbens and Donald B Rubin · 2015
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Regularization methods for high-dimensional instrumental variables regression with an application to genetical genomics
Wei Lin, Rui Feng, and Hongzhe Li · 2015
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Learning representations for counterfactual inference
Fredrik Johansson, Uri Shalit, and David Sontag · 2016
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Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling · 2016
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Treatment effect estimation with data-driven variable decomposition
Kun Kuang, Peng Cui, Bo Li, Meng Jiang, Shiqiang Yang, and Fei Wang · 2017
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Matching on balanced nonlinear representations for treatment effects estimation
Sheng Li and Yun Fu · 2017
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Estimation of causal effects with multiple treatments: a review and new ideas
Michael J Lopez and Roee Gutman · 2017
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Causal effect inference with deep latent-variable models
Christos Louizos, Uri Shalit, Joris Mooij, David Sontag, Richard Zemel, and Max Welling · 2017
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Estimating individual treatment effect: generalization bounds and algorithms
Uri Shalit, Fredrik D Johansson, and David Sontag · 2017
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Outcome-adaptive lasso: Variable selection for causal inference
Susan M Shortreed and Ashkan Ertefaie · 2017
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Estimation of causal effects of multiple treatments in observational studies with a binary outcome
Liangyuan Hu, Chenyang Gu, Michael Lopez, Jiayi Ji, and Juan Wisnivesky · 2020
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Deep structural causal models for tractable counterfactual inference
Nick Pawlowski, Daniel Coelho de Castro, and Ben Glocker · 2020
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Learning counterfactual representations for estimating individual dose-response curves
Patrick Schwab, Lorenz Linhardt, Stefan Bauer, Joachim M Buhmann, and Walter Karlen · 2020
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Learning decomposed representation for counterfactual inference
Anpeng Wu, Kun Kuang, Junkun Yuan, Bo Li, Runze Wu, Qiang Zhu, Yueting Zhuang, and Fei Wu · 2020
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Learning overlapping representations for the estimation of individualized treatment effects
Yao Zhang, Alexis Bellot, and Mihaela van der Schaar · 2020
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Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio · 2017
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Limits of estimating heterogeneous treatment effects: Guidelines for practical algorithm design
Ahmed Alaa and Mihaela Schaar · 2018
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A survey of learning causality with data: Problems and methods
Ruocheng Guo, Lu Cheng, Jundong Li, P Richard Hahn, and Huan Liu · 2018
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Patrick Schwab, Lorenz Linhardt, and Walter Karlen · 2018
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Representation learning for treatment effect estimation from observational data
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Ganite: Estimation of individualized treatment effects using generative adversarial nets
Jinsung Yoon, James Jordon, and Mihaela Van Der Schaar · 2018
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Learning individual treatment effects from networked observational data
Ruocheng Guo, Jundong Li, and Huan Liu · 2019
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Graph infomax adversarial learning for treatment effect estimation with networked observational data
Zhixuan Chu, Stephen L Rathbun, and Sheng Li · 2021
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Graphite: Estimating individual effects of graph-structured treatments
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Causal effect inference for structured treatments
Jean Kaddour, Yuchen Zhu, Qi Liu, Matt J Kusner, and Ricardo Silva · 2021
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Cycle self-training for domain adaptation
Hong Liu, Jianmin Wang, and Mingsheng Long · 2021
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Deconfounding with networked observational data in a dynamic environment
Jing Ma, Ruocheng Guo, Chen Chen, Aidong Zhang, and Jundong Li · 2021
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Vcnet and functional targeted regularization for learning causal effects of continuous treatments
Lizhen Nie, Mao Ye, Qiang Liu, and Dan Nicolae · 2021
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A survey on causal inference
Liuyi Yao, Zhixuan Chu, Sheng Li, Yaliang Li, Jing Gao, and Aidong Zhang · 2021
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Treatment effect estimation with disentangled latent factors
Weijia Zhang, Lin Liu, and Jiuyong Li · 2021
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Learning disentangled representations for counterfactual regression via mutual information minimization
Mingyuan Cheng, Xinru Liao, Quan Liu, Bin Ma, Jian Xu, and Bo Zheng · 2022
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Learning infomax and domain-independent representations for causal effect inference with real-world data
Zhixuan Chu, Stephen L Rathbun, and Sheng Li · 2022
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Multi-task adversarial learning for treatment effect estimation in basket trials
Zhixuan Chu, Stephen L Rathbun, and Sheng Li · 2022
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Causal inference in natural language processing: Estimation, prediction, interpretation and beyond
Amir Feder, Katherine A Keith, Emaad Manzoor, Reid Pryzant, Dhanya Sridhar, Zach Wood-Doughty, Jacob Eisenstein, Justin Grimmer, Roi Reichart, Margaret E Roberts, et al · 2022
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Image-based treatment effect heterogeneity
Connor T Jerzak, Fredrik Johansson, and Adel Daoud · 2022
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Causalegm: a general causal inference framework by encoding generative modeling
Qiao Liu, Zhongren Chen, and Wing Hung Wong · 2022
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Learning causality with graphs
Jing Ma and Jundong Li · 2022
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A survey of causal inference frameworks
Jingying Zeng and Run Wang · 2022
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Continual treatment effect estimation: Challenges and opportunities
Zhixuan Chu and Sheng Li · 2023
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Estimating propensity scores with deep adaptive variable selection
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