Relevant bibliographies by topics / Spurious correlations

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Spurious correlations'

Author: Grafiati
Published: 8 June 2024
Last updated: 31 July 2025

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Journal articles on the topic "Spurious correlations"

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Ben-Zeev, Talia, and Jon R. Star. "Spurious Correlations in Mathematical Thinking." Cognition and Instruction 19, no. 3 (2001): 253–75. http://dx.doi.org/10.1207/s1532690xci1903_1.

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Ward, Andrew. "“Spurious Correlations and Causal Inferences”." Erkenntnis 78, no. 3 (2012): 699–712. http://dx.doi.org/10.1007/s10670-012-9411-6.

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Jackson, D. A., and K. M. Somers. "The spectre of ?spurious? correlations." Oecologia 86, no. 1 (1991): 147–51. http://dx.doi.org/10.1007/bf00317404.

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Halperin, Silas. "Spurious correlations—causes and cures." Psychoneuroendocrinology 11, no. 1 (1986): 3–13. http://dx.doi.org/10.1016/0306-4530(86)90028-4.

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Lorenzo-Arribas, Altea, Penny S. Reynolds, and Chaitra H. Nagaraja. "Suffrage, Statistics, and Spurious Correlations." CHANCE 36, no. 4 (2023): 51–54. http://dx.doi.org/10.1080/09332480.2023.2290956.

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Fan, Jianqing, Qi-Man Shao, and Wen-Xin Zhou. "Are discoveries spurious? Distributions of maximum spurious correlations and their applications." Annals of Statistics 46, no. 3 (2018): 989–1017. http://dx.doi.org/10.1214/17-aos1575.

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Berges, John A. "Ratios, regression statistics, and “spurious” correlations." Limnology and Oceanography 42, no. 5 (1997): 1006–7. http://dx.doi.org/10.4319/lo.1997.42.5.1006.

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Pollman, Curtis D., and Donald M. Axelrad. "Mercury bioaccumulation factors and spurious correlations." Science of The Total Environment 496 (October 2014): vi—xii. http://dx.doi.org/10.1016/j.scitotenv.2014.07.050.

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Richman, Jesse T., and Ryan J. Roberts. "Assessing Spurious Correlations in Big Search Data." Forecasting 5, no. 1 (2023): 285–96. http://dx.doi.org/10.3390/forecast5010015.

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Big search data offers the opportunity to identify new and potentially real-time measures and predictors of important political, geographic, social, cultural, economic, and epidemiological phenomena, measures that might serve an important role as leading indicators in forecasts and nowcasts. However, it also presents vast new risks that scientists or the public will identify meaningless and totally spurious ‘relationships’ between variables. This study is the first to quantify that risk in the context of search data. We find that spurious correlations arise at exceptionally high frequencies am
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Sorjonen, Kimmo, Gustav Nilsonne, Michael Ingre, and Bo Melin. "Spurious correlations in research on ability tilt." Personality and Individual Differences 185 (February 2022): 111268. http://dx.doi.org/10.1016/j.paid.2021.111268.

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Dissertations / Theses on the topic "Spurious correlations"

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Dediu, Dan. "Non-Spurious Correlations Between Genetic and Linguistic Diversities in the Context of Human Evolution." Thesis, University of Edinburgh, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.490090.

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Tan, David Tatwei Banking &amp Finance Australian School of Business UNSW. "Corporate governance and firm outcomes: causation or spurious correlation?" Awarded By:University of New South Wales. Banking & Finance, 2009. http://handle.unsw.edu.au/1959.4/43371.

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The rapid growth of financial markets and the increasing diffusion of corporate ownership have placed tremendous emphasis on the effectiveness of corporate governance in resolving agency conflicts within the firm. This study investigates the corporate governance and firm performance/failure relation by implementing various econometric modelling methods to disaggregate causal relations and spurious correlations. Using a panel dataset of Australian firms, a comprehensive suite of corporate governance mechanisms are considered; including the ownership, remuneration, and board structures of the fi
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Bose, Tulika. "Transfer learning for abusive language detection." Electronic Thesis or Diss., Université de Lorraine, 2023. http://www.theses.fr/2023LORR0019.

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La prolifération des médias sociaux, malgré ses nombreux avantages, a entraîné une augmentation des propos injurieux. Ces propos, qui sont généralement blessants, toxiques ou empreints de préjugés à l'encontre d'individus ou de groupes, doivent être détectés et modérés rapidement par les plateformes en ligne. Les modèles d'apprentissage profond pour la détection de propos abusifs ont montré des niveaux de performance élevé quand ils sont évalués sur des données similaires à celles qui ont servi à entraîner les modèles, mais sont nettement moins performants s'ils sont évalués sur des données do
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Books on the topic "Spurious correlations"

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Spurious Correlations. Hachette Books, 2015.

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Vigen, Tyler. Spurious Correlations. Hachette Books, 2015.

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Book chapters on the topic "Spurious correlations"

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Evensen, Geir. "Spurious correlations, localization, and inflation." In Data Assimilation. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03711-5_15.

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Hagos, Misgina Tsighe, Kathleen M. Curran, and Brian Mac Namee. "Unlearning Spurious Correlations in Chest X-Ray Classification." In Discovery Science. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-45275-8_26.

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Kumar, Amar, Nima Fathi, Raghav Mehta, et al. "Debiasing Counterfactuals in the Presence of Spurious Correlations." In Clinical Image-Based Procedures, Fairness of AI in Medical Imaging, and Ethical and Philosophical Issues in Medical Imaging. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-45249-9_27.

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Bykov, Kirill, Laura Kopf, and Marina M. C. Höhne. "Finding Spurious Correlations with Function-Semantic Contrast Analysis." In Communications in Computer and Information Science. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-44067-0_28.

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Enström, Daniel, Viktor Kjellberg, and Moa Johansson. "Reasoning in Transformers – Mitigating Spurious Correlations and Reasoning Shortcuts." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-71170-1_18.

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Wu, Jiaying, and Bryan Hooi. "Probing Spurious Correlations in Popular Event-Based Rumor Detection Benchmarks." In Machine Learning and Knowledge Discovery in Databases. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-26390-3_17.

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Negri, Fedra. "Correlation Is Not Causation, Yet… Matching and Weighting for Better Counterfactuals." In Texts in Quantitative Political Analysis. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-12982-7_4.

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AbstractAnyone who has attended a statistics class has heard the old adage “correlation does not imply causation,” usually followed by a series of hilarious graphs showing spurious correlations. Even if we strongly agree with it, this reminder has been taken a little too far: it is repeated like a mantra to criticize every observational study as being unable to detect causation behind statistical association. This chapter helps the reader go beyond the mantra, firstly, by explaining that “correlation does not imply causation” in observational studies because of selection bias (i.e. the composi
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Kim, Jeeyung, Ze Wang, and Qiang Qiu. "Constructing Concept-Based Models to Mitigate Spurious Correlations with Minimal Human Effort." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-72989-8_8.

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Sun, Susu, Lisa M. Koch, and Christian F. Baumgartner. "Right for the Wrong Reason: Can Interpretable ML Techniques Detect Spurious Correlations?" In Lecture Notes in Computer Science. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-43895-0_40.

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Cheng, Yalong, Chuiyi Chen, Zeyu Nie, Zhipeng Liu, and Jun Liang. "Mitigating Spurious Correlations in Few-Shot Classification via Bias and Dynamic Prompt." In Lecture Notes in Computer Science. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-9866-0_4.

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Conference papers on the topic "Spurious correlations"

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Tiwari, Kshitiz, Shuhan Yuan, and Lu Zhang. "Robust Hate Speech Detection via Mitigating Spurious Correlations." In Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 2: Short Papers). Association for Computational Linguistics, 2022. http://dx.doi.org/10.18653/v1/2022.aacl-short.7.

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Liusie, Adian, Vatsal Raina, Vyas Raina, and Mark Gales. "Analyzing Biases to Spurious Correlations in Text Classification Tasks." In Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 2: Short Papers). Association for Computational Linguistics, 2022. http://dx.doi.org/10.18653/v1/2022.aacl-short.11.

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Gui, Lujun, and Chris Zhang. "Mitigating Spurious Correlations in Preclinical Alzheimer's Disease Detection via Pretraining." In 2024 17th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI). IEEE, 2024. https://doi.org/10.1109/cisp-bmei64163.2024.10906245.

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Ghosh, Sreyan, Chandra Kiran Evuru, Sonal Kumar, et al. "ASPIRE: Language-Guided Data Augmentation for Improving Robustness Against Spurious Correlations." In Findings of the Association for Computational Linguistics ACL 2024. Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.findings-acl.22.

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Zhou, Yuqing, and Ziwei Zhu. "Fighting Spurious Correlations in Text Classification via a Causal Learning Perspective." In Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). Association for Computational Linguistics, 2025. https://doi.org/10.18653/v1/2025.naacl-long.215.

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Liu, Jiawei, Min Huang, and Qinghai Miao. "Mitigating Spurious Correlations in Named Entity Recognition Models Through Counterfactual Data Augmentation." In 2024 International Joint Conference on Neural Networks (IJCNN). IEEE, 2024. http://dx.doi.org/10.1109/ijcnn60899.2024.10649919.

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Capitani, Giacomo, Lorenzo Bonicelli, Angelo Porrello, Federico Bolelli, Simone Calderara, and Elisa Ficarra. "Towards Unbiased Continual Learning: Avoiding Forgetting in the Presence of Spurious Correlations." In 2025 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). IEEE, 2025. https://doi.org/10.1109/wacv61041.2025.00251.

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Zhou, Yuhang, Paiheng Xu, Xiaoyu Liu, Bang An, Wei Ai, and Furong Huang. "Explore Spurious Correlations at the Concept Level in Language Models for Text Classification." In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.acl-long.28.

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Salama, Mamdouh M. "Performance of Sand Monitors." In CORROSION 2000. NACE International, 2000. https://doi.org/10.5006/c2000-00085.

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Abstract Sand monitoring is a key element of proper sand control and management. The paper present the results of studies that were undertaken to assess the sensitivity, accuracy, repeatability and reliability of several commercially available acoustic and electrical resistant (ER) sand monitors. The results showed that while commercially available monitors are capable of predicting sand production, they couldn’t achieve the desired quantitative sensitivities and accuracy using their internal calibration models. Improvement in the accuracy of the acoustic probes was achieved by in-situ calibra
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Zheng, Guangtao, Wenqian Ye, and Aidong Zhang. "Learning Robust Classifiers with Self-Guided Spurious Correlation Mitigation." In Thirty-Third International Joint Conference on Artificial Intelligence {IJCAI-24}. International Joint Conferences on Artificial Intelligence Organization, 2024. http://dx.doi.org/10.24963/ijcai.2024/619.

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Deep neural classifiers tend to rely on spurious correlations between spurious attributes of inputs and targets to make predictions, which could jeopardize their generalization capability. Training classifiers robust to spurious correlations typically relies on annotations of spurious correlations in data, which are often expensive to get. In this paper, we tackle an annotation-free setting and propose a self-guided spurious correlation mitigation framework. Our framework automatically constructs fine-grained training labels tailored for a classifier obtained with empirical risk minimization t
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