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The predictions of text classifiers are often driven by spurious correlations -- e.g., the term `Spielberg' correlates with positively reviewed movies, even though the term itself does not semantically convey a positive sentiment.
A backdoor attack against lstm-based text classification systems
Jiazhu Dai, Chuanshuai Chen, and Yike Guo. 2019 · 1905
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Correlations genuine and spurious in pearson and yule
John Aldrich et al. 1995 · 1995
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The estimation of causal effects from observational data
Christopher Winship and Stephen L Morgan. 1999 · 1999
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Nonparametric estimation of average treatment effects under exogeneity: A review
Guido W Imbens. 2004 · 2004
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Katherine A Keith, David Jensen, and Brendan O’Connor. 2020 · 2005
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An investigation of why overparameterization exacerbates spurious correlations
Shiori Sagawa, Aditi Raghunathan, Pang Wei Koh, and Percy Liang. 2020b · 2005
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Recognizing contextual polarity in phrase-level sentiment analysis
Theresa Wilson, Janyce Wiebe, and Paul Hoffmann. 2005 · 2005
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Using “annotator rationales” to improve machine learning for text categorization
Omar Zaidan, Jason Eisner, and Christine Piatko. 2007 · 2007
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Dataset shift in machine learning
Joaquin Quionero-Candela, Masashi Sugiyama, Anton Schwaighofer, and Neil D Lawrence. 2009 · 2009
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Matching methods for causal inference: A review and a look forward
Elizabeth A Stuart. 2010 · 2010
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Gary King, Richard Nielsen, Carter Coberley, James E. Pope, and Aaron Wells. 2011 · 2011
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Sentiment Analysis and Opinion Mining
Bing Liu. 2012 · 2012
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David Martens and Foster Provost. 2014 · 2014
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Towards a learning theory of cause-effect inference
David Lopez-Paz, Krikamol Muandet, Bernhard Schölkopf, and Iliya Tolstikhin. 2015 · 2015
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Active learning with rationales for text classification
Manali Sharma, Di Zhuang, and Mustafa Bilgic. 2015 · 2015
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Adversarial removal of demographic attributes from text data
Yanai Elazar and Yoav Goldberg. 2018 · 2018
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A survey of methods for explaining black box models
Riccardo Guidotti, Anna Monreale, Salvatore Ruggieri, Franco Turini, Fosca Giannotti, and Dino Pedreschi. 2018 · 2018
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Algorithmic fairness
Jon Kleinberg, Jens Ludwig, Sendhil Mullainathan, and Ashesh Rambachan. 2018 · 2018
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Robust text classification under confounding shift
Virgile Landeiro and Aron Culotta. 2018 · 2018
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Deconfounded lexicon induction for interpretable social science
Reid Pryzant, Kelly Shen, Dan Jurafsky, and Stefan Wagner. 2018 · 2018
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Challenges of using text classifiers for causal inference
Zach Wood-Doughty, Ilya Shpitser, and Mark Dredze. 2018 · 2018
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Ups and downs
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Targeted backdoor attacks on deep learning systems using data poisoning
Xinyun Chen, Chang Liu, Bo Li, Kimberly Lu, and Dawn Song. 2017 · 2017
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Feature selection as causal inference: Experiments with text classification
Michael J Paul. 2017 · 2017
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Ellery Wulczyn, Nithum Thain, and Lucas Dixon. 2017 · 2017
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Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018 · 2018
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Distributionally robust neural networks for group shifts: On the importance of regularization for worst-case generalization
Shiori Sagawa, Pang Wei Koh, Tatsunori B Hashimoto, and Percy Liang. 2020a
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Counterfactual fairness in text classification through robustness
Sahaj Garg, Vincent Perot, Nicole Limtiaco, Ankur Taly, Ed H Chi, and Alex Beutel. 2019 · 2019
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Why propensity scores should not be used for matching
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Topics to avoid: Demoting latent confounds in text classification
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