Journal articles: 'Spurious correlations'

1

Ben-Zeev, Talia, and Jon R. Star. "Spurious Correlations in Mathematical Thinking." Cognition and Instruction 19, no. 3 (September 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 (November 11, 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 (March 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 (January 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 (October 2, 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 (June 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 (July 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 (February 28, 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 among probability distributions examined for random variables based upon gamma (1, 1) and Gaussian random walk distributions. Quantifying these spurious correlations and their likely magnitude for various distributions has value for several reasons. First, analysts can make progress toward accurate inference. Second, they can avoid unwarranted credulity. Third, they can demand appropriate disclosure from the study authors.

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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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CUSHMAN, SAMUEL A., and ERIN L. LANDGUTH. "Spurious correlations and inference in landscape genetics." Molecular Ecology 19, no. 17 (July 7, 2010): 3592–602. http://dx.doi.org/10.1111/j.1365-294x.2010.04656.x.

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Li, Lei, Pierre Boué, and Michel Campillo. "Observation and explanation of spurious seismic signals emerging in teleseismic noise correlations." Solid Earth 11, no. 1 (January 30, 2020): 173–84. http://dx.doi.org/10.5194/se-11-173-2020.

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Abstract. Deep body waves have been reconstructed from seismic noise correlations in recent studies. The authors note their great potential for deep-Earth imaging. In addition to the expected physical seismic phases, some spurious arrivals having no correspondence in earthquake seismograms are observed from the noise correlations. The origins of the noise-derived body waves have not been well understood. Traditionally, the reconstruction of seismic phases from inter-receiver noise correlations is attributed to the interference between waves from noise sources in the stationary-phase regions. The interfering waves emanating from a stationary-phase location have a common ray path from the source to the first receiver. The correlation operator cancels the common path and extracts a signal corresponding to the inter-receiver ray path. In this study, with seismic noise records from two networks at teleseismic distance, we show that noise-derived spurious seismic signals without correspondence in real seismograms can arise from the interference between waves without a common ray path or common slowness. These noise-derived signals cannot be explained by traditional stationary-phase arguments. Numerical experiments reproduce the observed spurious signals. These signals still emerge for uniformly distributed noise sources, and thus are not caused by localized sources. We interpret the presence of the spurious signals with a less restrictive condition of quasi-stationary phase: providing the time delays between interfering waves from spatially distributed noise sources are close enough, the stack of correlation functions over the distributed sources can still be constructive as an effect of finite frequencies, and thereby noise-derived signals emerge from the source averaging.

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Freund, Jan A., and Alexander Cerquera. "How spurious correlations affect a correlation-based measure of spike timing reliability." Neurocomputing 81 (April 2012): 97–103. http://dx.doi.org/10.1016/j.neucom.2011.10.014.

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Dean, Thomas J., and Quang V. Cao. "Inherent Correlations Between Stand Biomass Variables Calculated from Tree Measurements." Forest Science 49, no. 2 (April 1, 2003): 279–84. http://dx.doi.org/10.1093/forestscience/49.2.279.

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Abstract Correlating stand-level variables is an important component of forest production ecology; however, correlations among variables calculated with equations having common independent variables are potentially spurious. Monte Carlo simulation techniques were used to determine the inherent or null correlation coefficients among stand-level biomass variables calculated with published, individual-tree equations using loblolly pine (Pinus taeda L.) data. Null correlations of foliage mass/ha with branch mass/ha, stem mass/ha, and periodic annual increments of biomass were high with similar equation forms and exponents in the equations. Most, but not all, correlation coefficients of foliage mass/ha with other biomass components and periodic annual increments of biomass were significantly different from the corresponding, null correlation coefficients. Stating the probability of a greater difference between the observed and the null correlation coefficients proved crucial in distinguishing between potentially meaningful and spurious correlations because in many cases, the observed correlation coefficients were close to the null values. Interpretation of the correlations among stand variables varied with the equations used to predict the variables. Consequently, in addition to comparing correlation coefficients to appropriate null values, conclusions drawn from the correlation among stand-level variables depend on the accuracy and precision of the equations used to calculate them. FOR. SCI. 49(2):279–284.

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Ninomiya, Yoshiyuki, Satoshi Kuriki, Toshihiko Shiroishi, and Toyoyuki Takada. "A modification of MaxT procedure using spurious correlations." Journal of Statistical Planning and Inference 214 (September 2021): 128–38. http://dx.doi.org/10.1016/j.jspi.2021.02.001.

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Lehrer, David, Janine Leschke, Stefan Lhachimi, Ana Vasiliu, and Brigitte Weiffen. "Brochures/Reviews/Reports: The Journal of Spurious Correlations." Bulletin of Sociological Methodology/Bulletin de Méthodologie Sociologique 94, no. 1 (April 2007): 95–98. http://dx.doi.org/10.1177/075910630709400109.

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Aldrich, John. "Correlations Genuine and Spurious in Pearson and Yule." Statistical Science 10, no. 4 (November 1995): 364–76. http://dx.doi.org/10.1214/ss/1177009870.

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Calude, Cristian S., and Giuseppe Longo. "The Deluge of Spurious Correlations in Big Data." Foundations of Science 22, no. 3 (March 7, 2016): 595–612. http://dx.doi.org/10.1007/s10699-016-9489-4.

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Tu, Lifu, Garima Lalwani, Spandana Gella, and He He. "An Empirical Study on Robustness to Spurious Correlations using Pre-trained Language Models." Transactions of the Association for Computational Linguistics 8 (October 2020): 621–33. http://dx.doi.org/10.1162/tacl_a_00335.

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Recent work has shown that pre-trained language models such as BERT improve robustness to spurious correlations in the dataset. Intrigued by these results, we find that the key to their success is generalization from a small amount of counterexamples where the spurious correlations do not hold. When such minority examples are scarce, pre-trained models perform as poorly as models trained from scratch. In the case of extreme minority, we propose to use multi-task learning (MTL) to improve generalization. Our experiments on natural language inference and paraphrase identification show that MTL with the right auxiliary tasks significantly improves performance on challenging examples without hurting the in-distribution performance. Further, we show that the gain from MTL mainly comes from improved generalization from the minority examples. Our results highlight the importance of data diversity for overcoming spurious correlations. 1

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Coyne, Joseph O. C., Sophia Nimphius, Robert U. Newton, and G. Gregory Haff. "Does Mathematical Coupling Matter to the Acute to Chronic Workload Ratio? A Case Study From Elite Sport." International Journal of Sports Physiology and Performance 14, no. 10 (November 1, 2019): 1447–54. http://dx.doi.org/10.1123/ijspp.2018-0874.

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Purpose: Criticisms of the acute to chronic workload ratio (ACWR) have been that the mathematical coupling inherent in the traditional calculation of the ACWR results in a spurious correlation. The purposes of this commentary are (1) to examine how mathematical coupling causes spurious correlations and (2) to use a case study from actual monitoring data to determine how mathematical coupling affects the ACWR. Methods: Training and competition workload (TL) data were obtained from international-level open-skill (basketball) and closed-skill (weightlifting) athletes before their respective qualifying tournaments for the 2016 Olympic Games. Correlations between acute TL, chronic TL, and the ACWR as coupled/uncoupled variations were examined. These variables were also compared using both rolling averages and exponentially weighted moving averages to account for any potential benefits of one calculation method over another. Results: Although there were some significant differences between coupled and uncoupled chronic TL and ACWR data, the effect sizes of these differences were almost all trivial (g = 0.04–0.21). Correlations ranged from r = .55 to .76, .17 to .53, and .88 to .99 for acute to chronic TL, acute to uncoupled chronic TL, and ACWR to uncoupled ACWR, respectively. Conclusions: There may be low risk of mathematical coupling causing spurious correlations in the TL–injury-risk relationship. Varying levels of correlation seem to exist naturally between acute and chronic TL variables regardless of coupling. The trivial to small effect sizes and large to nearly perfect correlations between coupled and uncoupled AWCRs also imply that mathematical coupling may have little effect on either calculation method, if practitioners choose to apply the ACWR for TL monitoring purposes.

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Bai, Yulong, Xiaoyan Ma, and Lin Ding. "A Fuzzy-Logic-Based Covariance Localization Method in Data Assimilation." Atmosphere 11, no. 10 (October 1, 2020): 1055. http://dx.doi.org/10.3390/atmos11101055.

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In ensemble data assimilation systems, the impracticalities of full sampling and systematic error often lead to spurious correlations between two variables with low actual correlations. To solve these problems, researchers have previously proposed a covariance localization (CL) method, which mainly involves the Schur product between a state error covariance matrix and a distance-based correlation matrix. Although this CL method can reduce spurious correlations to a certain extent, observational data remain difficult to be used effectively, which results in unreasonable assimilation. In this study, we develop a new CL method coupled with a fuzzy logic control algorithm, which we call the covariance fuzzy (CF) method. The proposed CF method is a distance-based localization method with “fuzzy” vanishing correlations in data assimilation (DA) systems. To verify the effectiveness of the new algorithm, we conducted a set of experiments using an ensemble Kalman filter (EnKF) that combines the nonlinear Lorenz-96 model or the quasi-geostrophic (QG) models. First, the performances of the CL and CF methods are discussed with respect to different strength forcings, ensemble sizes, and covariance inflation factors. The experimental results show that the proposed CF method can obtain a more effective observation weight than the CL method and can reduce the errors caused by spurious correlations. Additionally, using power spectral density (PSD) as a performance evaluation index, the robustness of the proposed fuzzy logic localization method is demonstrated. However, the application of the fuzzy logic-based localization methodology to a real atmospheric model remains to be tested.

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Zanos, Theodoros P., Patrick J. Mineault, and Christopher C. Pack. "Removal of Spurious Correlations Between Spikes and Local Field Potentials." Journal of Neurophysiology 105, no. 1 (January 2011): 474–86. http://dx.doi.org/10.1152/jn.00642.2010.

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Single neurons carry out important sensory and motor functions related to the larger networks in which they are embedded. Understanding the relationships between single-neuron spiking and network activity is therefore of great importance and the latter can be readily estimated from low-frequency brain signals known as local field potentials (LFPs). In this work we examine a number of issues related to the estimation of spike and LFP signals. We show that spike trains and individual spikes contain power at the frequencies that are typically thought to be exclusively related to LFPs, such that simple frequency-domain filtering cannot be effectively used to separate the two signals. Ground-truth simulations indicate that the commonly used method of estimating the LFP signal by low-pass filtering the raw voltage signal leads to artifactual correlations between spikes and LFPs and that these correlations exert a powerful influence on popular metrics of spike–LFP synchronization. Similar artifactual results were seen in data obtained from electrophysiological recordings in macaque visual cortex, when low-pass filtering was used to estimate LFP signals. In contrast LFP tuning curves in response to sensory stimuli do not appear to be affected by spike contamination, either in simulations or in real data. To address the issue of spike contamination, we devised a novel Bayesian spike removal algorithm and confirmed its effectiveness in simulations and by applying it to the electrophysiological data. The algorithm, based on a rigorous mathematical framework, outperforms other methods of spike removal on most metrics of spike–LFP correlations. Following application of this spike removal algorithm, many of our electrophysiological recordings continued to exhibit spike–LFP correlations, confirming previous reports that such relationships are a genuine aspect of neuronal activity. Overall, these results show that careful preprocessing is necessary to remove spikes from LFP signals, but that when effective spike removal is used, spike–LFP correlations can potentially yield novel insights about brain function.

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Qian, Jiang, Yuval Kluger, Haiyuan Yu, and Mark Gerstein. "Identification and correction of spurious spatial correlations in microarray data." BioTechniques 35, no. 1 (July 2003): 42–48. http://dx.doi.org/10.2144/03351bm03.

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Leuridan, B., E. Weber, and M. Van Dyck. "The practical value of spurious correlations: selective versus manipulative policy." Analysis 68, no. 4 (October 1, 2008): 298–303. http://dx.doi.org/10.1093/analys/68.4.298.

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Rodgers, Joseph L. "Birth order, SAT, and confluence: Spurious correlations and no causality." American Psychologist 43, no. 6 (1988): 476–77. http://dx.doi.org/10.1037/0003-066x.43.6.476.

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Meiser, Thorsten, and Miles Hewstone. "Contingency learning and stereotype formation: Illusory and spurious correlations revisited." European Review of Social Psychology 21, no. 1 (March 2010): 285–331. http://dx.doi.org/10.1080/10463283.2010.543308.

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Zhang, Lily H., and Rajesh Ranganath. "Robustness to Spurious Correlations Improves Semantic Out-of-Distribution Detection." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 12 (June 26, 2023): 15305–12. http://dx.doi.org/10.1609/aaai.v37i12.26785.

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Methods which utilize the outputs or feature representations of predictive models have emerged as promising approaches for out-of-distribution (OOD) detection of image inputs. However, as demonstrated in previous work, these methods struggle to detect OOD inputs that share nuisance values (e.g. background) with in-distribution inputs. The detection of shared-nuisance OOD (SN-OOD) inputs is particularly relevant in real-world applications, as anomalies and in-distribution inputs tend to be captured in the same settings during deployment. In this work, we provide a possible explanation for these failures and propose nuisance-aware OOD detection to address them. Nuisance-aware OOD detection substitutes a classifier trained via Empirical Risk Minimization (ERM) with one that 1. approximates a distribution where the nuisance-label relationship is broken and 2. yields representations that are independent of the nuisance under this distribution, both marginally and conditioned on the label. We can train a classifier to achieve these objectives using Nuisance-Randomized Distillation (NuRD), an algorithm developed for OOD generalization under spurious correlations. Output- and feature-based nuisance-aware OOD detection perform substantially better than their original counterparts, succeeding even when detection based on domain generalization algorithms fails to improve performance.

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Retailleau, Lise, Pierre Boué, Laurent Stehly, and Michel Campillo. "Locating Microseism Sources Using Spurious Arrivals in Intercontinental Noise Correlations." Journal of Geophysical Research: Solid Earth 122, no. 10 (October 2017): 8107–20. http://dx.doi.org/10.1002/2017jb014593.

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Jackson, Donald A., Harold H. Harvey, and Keith M. Somers. "Ratios in Aquatic Sciences: Statistical Shortcomings with Mean Depth and the Morphoedaphic Index." Canadian Journal of Fisheries and Aquatic Sciences 47, no. 9 (September 1, 1990): 1788–95. http://dx.doi.org/10.1139/f90-203.

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Researchers in aquatic sciences frequently employ empirically derived models to predict productivity, yield, and abundance of fish. We demonstrate that predictive models employing ratios of standardized biomass and lake morphometric variables are biased by spurious correlations due to mathematical transformations and the use of inappropriate null models. Our findings emphasise that studies incorporating ratios like mean depth or the morphoedaphic index require cautious interpretation. Future research should focus on more appropriate analytical approaches such as regression-based models like the analysis of covariance. Alternatively, where ratios are employed and spurious correlations are likely, statistical evaluations must incorporate randomization tests to assess the significance of such results.

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LIN, AIJING, and PENGJIAN SHANG. "MINIMIZING PERIODIC TRENDS BY APPLYING LAPLACE TRANSFORM." Fractals 19, no. 02 (June 2011): 203–11. http://dx.doi.org/10.1142/s0218348x11005245.

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Rescaled range analysis (R/S analysis), detrended fluctuation analysis (DFA) and detrended moving average (DMA) are widely-used methods for detection of long-range correlations in time series. Detrended cross-correlation analysis (DCCA) is a recently developed method to quantify the cross-correlations of two non-stationary time series. Another method for studying auto-correlations and cross-correlations was presented by Sergio Arianos and Anna Carbone in 2009. Recent studies have reported the susceptibility of this methods to periodic trends, which can result in spurious crossovers. In this paper, we propose the modified methods base on Laplace transform to minimizing the effect of periodic trends. The effectiveness of our techniques are demonstrated on stock data corrupted with periodic trends.

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POLYAKOV, A. M. "SELF-TUNING FIELDS AND RESONANT CORRELATIONS IN 2d-GRAVITY." Modern Physics Letters A 06, no. 07 (March 7, 1991): 635–44. http://dx.doi.org/10.1142/s0217732391000658.

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We show that minimal models coupled to quantum gravity have special type of resonant correlations defined by the free field representations and are explicitly computable. The key phenomena in these models are shown to be discontinuity of the number of degrees of freedom at exceptional values of momenta and incomplete decoupling of the spurious states.

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Chen, Yan, and Dean S. Oliver. "Localization of Ensemble-Based Control-Setting Updates for Production Optimization." SPE Journal 17, no. 01 (July 11, 2011): 122–36. http://dx.doi.org/10.2118/125042-pa.

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Summary In the ensemble-based approach to production optimization (EnOpt), a steepest-ascent direction is computed from an ensemble of controls to iteratively improve a set of control settings. The method was shown to work well in maximizing field net present value (NPV) with an ensemble size of 104 on the Brugge SPE comparative test case for closed-loop optimization that had 84 controllable completion intervals (and 3,360 control variables), but performance of the method with smaller ensemble size or on larger problems might be difficult. Without regularization, the crosscovariance between control variables and the objective function is often likely to be dominated by spurious correlations. Because the update to the control variables is proportional to the covariance, spurious correlations will result in poor control settings. We propose a localization method that updates the control setting to optimize the field production while reconciling information from each individual well. The proposed localization method reduces the effect of spurious correlations for improved performance. The Brugge test case is used as an example to show that with covariance localization, greater efficiency could be achieved through the use of a smaller ensemble, or that for a given ensemble size, the optimization results can be improved.

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Calude, Cristian S., and Karl Svozil. "Spurious, Emergent Laws in Number Worlds." Philosophies 4, no. 2 (April 16, 2019): 17. http://dx.doi.org/10.3390/philosophies4020017.

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We study some aspects of the emergence of lógos from xáos on a basal model of the universe using methods and techniques from algorithmic information and Ramsey theories. Thereby an intrinsic and unusual mixture of meaningful and spurious, emerging laws surfaces. The spurious, emergent laws abound, they can be found almost everywhere. In accord with the ancient Greek theogony one could say that lógos, the Gods and the laws of the universe, originate from “the void,” or from xáos, a picture which supports the unresolvable/irreducible lawless hypothesis. The analysis presented in this paper suggests that the “laws” discovered in science correspond merely to syntactical correlations, are local and not universal.

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Andersson, Lars. "Loneliness and its Relationship with Misery." Psychological Reports 73, no. 2 (October 1993): 584–86. http://dx.doi.org/10.2466/pr0.1993.73.2.584.

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Potter, Douglas M. "Comment on Mesoudi and O’Brien’s “The Cultural Transmission of Great Basin Projectile-Point Technology I: An Experimental Simulation”." American Antiquity 77, no. 2 (April 2012): 368–71. http://dx.doi.org/10.7183/0002-7316.77.2.368.

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AbstractMesoudi and O’Brien (2008) describe an experimental simulation of the cultural transmission of projectile-point technology. They base some of their conclusions on the results of tests of correlation. I explain why their use of correlation is improper, and hence that the validity of their conclusions is questionable at best. To support that assertion, I provide simulations to demonstrate that spurious correlations arise in the situations where the authors use correlation. I also describe analysis methods appropriate for their data.

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Pan, Yonghua, Zechao Li, Liyan Zhang, and Jinhui Tang. "Causal Inference with Knowledge Distilling and Curriculum Learning for Unbiased VQA." ACM Transactions on Multimedia Computing, Communications, and Applications 18, no. 3 (August 31, 2022): 1–23. http://dx.doi.org/10.1145/3487042.

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Recently, many Visual Question Answering (VQA) models rely on the correlations between questions and answers yet neglect those between the visual information and the textual information. They would perform badly if the handled data distribute differently from the training data (i.e., out-of-distribution (OOD) data). Towards this end, we propose a two-stage unbiased VQA approach that addresses the unbiased issue from a causal perspective. In the causal inference stage, we mark the spurious correlation on the causal graph, explore the counterfactual causality, and devise a causal target based on the inherent correlations between the conventional and counterfactual VQA models. In the distillation stage, we introduce the causal target into the training process and leverages distilling as well as curriculum learning to capture the unbiased model. Since Causal Inference with Knowledge Distilling and Curriculum Learning (CKCL) reinforces the contribution of the visual information and eliminates the impact of the spurious correlation by distilling the knowledge in causal inference to the VQA model, it contributes to the good performance on both the standard data and out-of-distribution data. The extensive experimental results on VQA-CP v2 dataset demonstrate the superior performance of the proposed method compared to the state-of-the-art (SotA) methods.

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Alvera-Azcárate, A., A. Barth, D. Sirjacobs, and J. M. Beckers. "Enhancing temporal correlations in EOF expansions for the reconstruction of missing data using DINEOF." Ocean Science 5, no. 4 (October 28, 2009): 475–85. http://dx.doi.org/10.5194/os-5-475-2009.

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Abstract. DINEOF (Data Interpolating Empirical Orthogonal Functions) is an EOF-based technique for the reconstruction of missing data in geophysical fields, such as those produced by clouds in sea surface temperature satellite images. A technique to reduce spurious time variability in DINEOF reconstructions is presented. The reconstruction of these images within a long time series using DINEOF can lead to large discontinuities in the reconstruction. Filtering the temporal covariance matrix allows to reduce this spurious variability and therefore more realistic reconstructions are obtained. The approach is tested in a three years sea surface temperature data set over the Black Sea. The effect of the filter in the temporal EOFs is presented, as well as some examples of the improvement achieved with the filtering in the SST reconstruction, both compared to the DINEOF approach without filtering.

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Alvera-Azcárate, A., A. Barth, D. Sirjacobs, and J. M. Beckers. "Enhancing temporal correlations in EOF expansions for the reconstruction of missing data using DINEOF." Ocean Science Discussions 6, no. 2 (July 9, 2009): 1547–68. http://dx.doi.org/10.5194/osd-6-1547-2009.

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Abstract. DINEOF (Data Interpolating Empirical Orthogonal Functions) is an EOF-based technique for the reconstruction of missing data in geophysical fields, such as those produced by clouds in sea surface temperature satellite images. A technique to reduce spurious time variability in DINEOF reconstructions is presented. The reconstruction of these images within a long time series using DINEOF can lead to large discontinuities in the reconstruction. Filtering the temporal covariance matrix allows to reduce this spurious variability and therefore more realistic reconstructions are obtained. The approach is tested in a three years sea surface temperature data set over the Black Sea. The effect of the filter in the temporal EOFs is presented, as well as some examples of the improvement achieved with the filtering in the SST reconstruction, both compared to the DINEOF approach without filtering.

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Zhao, Zixuan, Hengzhuang Li, Jinxuan Chen, Yang Li, and Jiayun Song. "Feature selection in text classification: Identifying spurious words with causal inference methods." Applied and Computational Engineering 6, no. 1 (June 14, 2023): 1522–32. http://dx.doi.org/10.54254/2755-2721/6/20230334.

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As has been scrutinized by many, non-causal model may contain spurious correlations that act like shortcuts during the prediction phase, undermining cross-domain accuracy. This can be caused by biased training data that contains spurious words with neutral meanings yet can induce the model to predict wrongly. Based on this assumption, we propose a series of methods to detect these spurious words before feeding the model with the training data. We used advanced causal inference methods which are arising novas in recent studies, such as propensity score matching and inverse propensity score weighting to facilitate the feature selection before training. We experimented with multiple approaches to estimate propensity scores and got profound improvements. We further experimented with BERT model to evaluate the effectiveness of feature selection and find that the model performance with in-domain and out-of-domain testing samples is boosted after we remove the spurious words detected by our methods in the training data.

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Wang, Zhao, and Aron Culotta. "Robustness to Spurious Correlations in Text Classification via Automatically Generated Counterfactuals." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 16 (May 18, 2021): 14024–31. http://dx.doi.org/10.1609/aaai.v35i16.17651.

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Spurious correlations threaten the validity of statistical classifiers. While model accuracy may appear high when the test data is from the same distribution as the training data, it can quickly degrade when the test distribution changes. For example, it has been shown that classifiers perform poorly when humans make minor modifications to change the label of an example. One solution to increase model reliability and generalizability is to identify causal associations between features and classes. In this paper, we propose to train a robust text classifier by augmenting the training data with automatically generated counterfactual data. We first identify likely causal features using a statistical matching approach. Next, we generate counterfactual samples for the original training data by substituting causal features with their antonyms and then assigning opposite labels to the counterfactual samples. Finally, we combine the original data and counterfactual data to train a robust classifier. Experiments on two classification tasks show that a traditional classifier trained on the original data does very poorly on human-generated counterfactual samples (e.g., 10%-37% drop in accuracy). However, the classifier trained on the combined data is more robust and performs well on both the original test data and the counterfactual test data (e.g., 12%-25% increase in accuracy compared with the traditional classifier). Detailed analysis shows that the robust classifier makes meaningful and trustworthy predictions by emphasizing causal features and de-emphasizing non-causal features.

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Sorjonen, Kimmo, and Bo Melin. "Spurious correlations in research on the effects of specific cognitive abilities." Personality and Individual Differences 187 (March 2022): 111417. http://dx.doi.org/10.1016/j.paid.2021.111417.

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Pawliuk, Micheal, and Michael Waddell. "Using Ramsey Theory to Measure Unavoidable Spurious Correlations in Big Data." Axioms 8, no. 1 (March 5, 2019): 29. http://dx.doi.org/10.3390/axioms8010029.

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Abstract:

Given a dataset, we quantify the size of patterns that must always exist in the dataset. This is done formally through the lens of Ramsey theory of graphs, and a quantitative bound known as Goodman’s theorem. By combining statistical tools with Ramsey theory of graphs, we give a nuanced understanding of how far away a dataset is from correlated, and what qualifies as a meaningful pattern. This method is applicable to a wide range of datasets. As examples, we analyze two very different datasets. The first is a dataset of repeated voters ( n = 435 ) in the 1984 US congress, and we quantify how homogeneous a subset of congressional voters is. We also measure how transitive a subset of voters is. Statistical Ramsey theory is also used with global economic trading data ( n = 214 ) to provide evidence that global markets are quite transitive. While these datasets are small relative to Big Data, they illustrate the new applications we are proposing. We end with specific calls to strengthen the connections between Ramsey theory and statistical methods.

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Fellner, M.-C., G. Volberg, K. J. Mullinger, M. Goldhacker, M. Wimber, M. W. Greenlee, and S. Hanslmayr. "Spurious correlations in simultaneous EEG-fMRI driven by in-scanner movement." NeuroImage 133 (June 2016): 354–66. http://dx.doi.org/10.1016/j.neuroimage.2016.03.031.

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Hsu, Louis M. "Correcting Correlations of Personality Scales for Spurious Effects of Shared Items." Multivariate Behavioral Research 27, no. 1 (January 1992): 31–41. http://dx.doi.org/10.1207/s15327906mbr2701_3.

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Liu, Li, and Richard J. Caselli. "Unbalanced Sample Size Introduces Spurious Correlations to Genome-Wide Heterozygosity Analyses." Human Heredity 84, no. 4-5 (2019): 197–202. http://dx.doi.org/10.1159/000507576.

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Schmidt, Gavin A. "Spurious correlations between recent warming and indices of local economic activity." International Journal of Climatology 29, no. 14 (January 7, 2009): 2041–48. http://dx.doi.org/10.1002/joc.1831.

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Schaworonkow, Natalie, Duncan A. J. Blythe, Jewgeni Kegeles, Gabriel Curio, and Vadim V. Nikulin. "Power-law dynamics in neuronal and behavioral data introduce spurious correlations." Human Brain Mapping 36, no. 8 (April 30, 2015): 2901–14. http://dx.doi.org/10.1002/hbm.22816.

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Håkanson, Lars, and Maria I. Stenström-Khalili. "Uncertainties in Data and Spurious Correlations Related to the Redfield Ratio." International Review of Hydrobiology 94, no. 3 (June 2009): 338–51. http://dx.doi.org/10.1002/iroh.200811110.

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Pan, Yue, Hua Hou, Xiaolong Pei, and Yuhong Zhao. "Feature purify: An examination of spurious correlations in high-entropy alloys." Materials & Design 239 (March 2024): 112785. http://dx.doi.org/10.1016/j.matdes.2024.112785.

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Páez, Antonio, Steven Farber, and David Wheeler. "A Simulation-Based Study of Geographically Weighted Regression as a Method for Investigating Spatially Varying Relationships." Environment and Planning A: Economy and Space 43, no. 12 (January 1, 2011): 2992–3010. http://dx.doi.org/10.1068/a44111.

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Abstract:

Large variability and correlations among the coefficients obtained from the method of geographically weighted regression (GWR) have been identified in previous research. This is an issue that poses a serious challenge for the utility of the method as a tool to investigate multivariate relationships. The objectives of this paper are to assess: (1) the ability of GWR to discriminate between a spatially constant processes and one with spatially varying relationships; and (2) to accurately retrieve spatially varying relationships. Extensive numerical experiments are used to investigate situations where the underlying process is stationary and nonstationary, and to assess the degree to which spurious intercoefficient correlations are introduced. Two different implementations of GWR and cross-validation approaches are assessed. Results suggest that judicious application of GWR can be used to discern whether the underlying process is nonstationary. Furthermore, evidence of spurious correlations indicates that caution must be exercised when drawing conclusions regarding spatial relationships retrieved using this approach, particularly when working with small samples.

Journal articles on the topic 'Spurious correlations'

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