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Spurious correlations are a threat to the trustworthiness of natural language processing systems, motivating research into methods for identifying and eliminating them.
Spurious correlation: A causal interpretation
Herbert A Simon. 1954 · 1954
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Douglas Biber. 1991 · 1991
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Causal diagrams for empirical research
Judea Pearl. 1995 · 1995
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Judea Pearl. 2009 · 2009
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On causal and anticausal learning
Bernhard Schölkopf, Dominik Janzing, Jonas Peters, Eleni Sgouritsa, Kun Zhang, and Joris Mooij. 2012 · 2012
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Elements of causal inference: foundations and learning algorithms
Jonas Peters, Dominik Janzing, and Bernhard Schölkopf. 2017 · 2017
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Annotation artifacts in natural language inference data
Suchin Gururangan, Swabha Swayamdipta, Omer Levy, Roy Schwartz, Samuel Bowman, and Noah A Smith. 2018 · 2018
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Hypothesis only baselines in natural language inference
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Causal direction of data collection matters: Implications of causal and anticausal learning for NLP
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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, Brandon M. Stewart, Victor Veitch, and Diyi Yang. 2021 · 2021
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Counterfactual invariance to spurious correlations in text classification
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On the limitations of dataset balancing: The lost battle against spurious correlations
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