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Debiased collaborative filtering aims to learn an unbiased prediction model by removing different biases in observational datasets.
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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The principle of maximum entropy
Silviu Guiasu and Abe Shenitzer · 1985
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Matrix factorization techniques for recommender systems
Yehuda Koren, Robert Bell, and Chris Volinsky · 2009
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Collaborative prediction and ranking with non-random missing data
Benjamin M Marlin and Richard S Zemel · 2009
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Training and testing low-degree polynomial data mappings via linear svm
Yin-Wen Chang, Cho-Jui Hsieh, Kai-Wei Chang, Michael Ringgaard, and Chih-Jen Lin · 2010
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Training and testing of recommender systems on data missing not at random
Harald Steck · 2010
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Matching methods for causal inference: A review and a look forward
Elizabeth A. Stuart · 2010
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Universality, characteristic kernels and rkhs embedding of measures
Bharath K Sriperumbudur, Kenji Fukumizu, and Gert RG Lanckriet · 2011
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Entropy balancing for causal effects: A multivariate reweighting method to produce balanced samples in observational studies
Jens Hainmueller · 2012
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Probabilistic matrix factorization with non-random missing data
José Miguel Hernández-Lobato, Neil Houlsby, and Zoubin Ghahramani · 2014
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Covariate balancing propensity score
Kosuke Imai and Marc Ratkovic · 2014
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Understanding machine learning: From theory to algorithms
Shai Shalev-Shwartz and Shai Ben-David · 2014
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Causal Inference For Statistics Social and Biomedical Science
Guido W. Imbens and Donald B. Rubin · 2015
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Counterfactuals and Causal Inference: Methods and Principles for Social Research
Stephen L. Morgan and Christopher Winship · 2015
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Recommendations as treatments: Debiasing learning and evaluation
Tobias Schnabel, Adith Swaminathan, Ashudeep Singh, Navin Chandak, and Thorsten Joachims · 2016
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Entropy balancing is doubly robust
Qingyuan Zhao and Daniel Percival · 2017
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Social recommendation with missing not at random data
Jiawei Chen, Can Wang, Martin Ester, Qihao Shi, Yan Feng, and Chun Chen · 2018
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Balancing covariates via propensity score weighting
Fan Li, Kari Lock Morgan, and Alan M Zaslavsky · 2018
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Foundations of machine learning
Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar · 2018
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Kernel-based covariate functional balancing for observational studies
Raymond K W Wong and Kwun Chuen Gary Chan · 2018
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Doubly robust joint learning for recommendation on data missing not at random
Xiaojie Wang, Rui Zhang, Yu Sun, and Jianzhong Qi · 2019
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Combating selection biases in recommender systems with a few unbiased ratings
Xiaojie Wang, Rui Zhang, Yu Sun, and Jianzhong Qi · 2021
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A generalized doubly robust learning framework for debiasing post-click conversion rate prediction
Quanyu Dai, Haoxuan Li, Peng Wu, Zhenhua Dong, Xiao-Hua Zhou, Rui Zhang, Xiuqiang He, Rui Zhang, and Jie Sun · 2022
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Reaching the end of unbiasedness: Uncovering implicit limitations of click-based learning to rank
Harrie Oosterhuis · 2022
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Covariate distribution balance via propensity scores
Pedro H. C. Sant’Anna, Xiaojun Song, and Qi Xu · 2022
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Weighted euclidean balancing for a matrix exposure in estimating causal effect
Juan Chen and Yingchun Zhou · 2023
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Duet: A tuning-free device-cloud collaborative parameters generation framework for efficient device model generalization
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Factual observation based heterogeneity learning for counterfactual prediction
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Prompt-based distribution alignment for unsupervised domain adaptation
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Out-of-distribution generalization with causal feature separation
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