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1. Contamination in theory and protest

   https://doi.org/10.1111/amet.13035  

   BOND, DAVID (November 2021, American Ethnologist)
2. Presumptive Contamination: A New Approach to PFAS Contamination Based on Likely Sources

   https://doi.org/10.1021/acs.estlett.2c00502  

   Salvatore, Derrick; Mok, Kira; Garrett, Kimberly K.; Poudrier, Grace; Brown, Phil; Birnbaum, Linda S.; Goldenman, Gretta; Miller, Mark F.; Patton, Sharyle; Poehlein, Maddy; et al (November 2022, Environmental Science & Technology Letters)
3. Sieve-element differentiation and phloem sap contamination

   Michael Knoblauch, Winfried S. (January 2018, Current opinion in plant biology)

   Sieve elements (SEs) degrade selected organelles and cytoplasmic structures when they differentiate. According to classical investigations, only smooth ER, mitochondria, sieve element plastids, and, in most cases, P-proteins remain in mature SEs. More recent proteomics and immunohistochemical studies, however, suggested that additional components including a protein-synthesizing machinery and a fully developed actin cytoskeleton operate in mature SEs. These interpretations are at odds with conventional imaging studies. Here we discuss potential causes for these discrepancies, concluding that differentiating SEs may play a role by ‘contaminating’ phloem exudates. 

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4. Contamination Bias in Linear Regressions

   https://doi.org/10.1257/aer.20221116  

   Goldsmith-Pinkham, Paul; Hull, Peter; Kolesár, Michal (December 2024, American Economic Review)

   We study regressions with multiple treatments and a set of controls that is flexible enough to purge omitted variable bias. We show these regressions generally fail to estimate convex averages of heterogeneous treatment effects—instead, estimates of each treatment’s effect are contaminated by nonconvex averages of the effects of other treatments. We discuss three estimation approaches that avoid such contamination bias, including the targeting of easiest-to-estimate weighted average effects. A reanalysis of nine empirical applications finds economically and statistically meaningful contamination bias in observational studies; contamination bias in experimental studies is more limited due to smaller variability in propensity scores. 

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5. Decontamination of Mutual Contamination Models

   Katz-Samuels, Julian; Blanchard, Gilles; Scott, Clayton (January 2019, Journal of machine learning research)

   Many machine learning problems can be characterized by mutual contamination models. In these problems, one observes several random samples from different convex combinations of a set of unknown base distributions and the goal is to infer these base distributions. This paper considers the general setting where the base distributions are defined on arbitrary probability spaces. We examine three popular machine learning problems that arise in this general setting: multiclass classification with label noise, demixing of mixed membership models, and classification with partial labels. In each case, we give sufficient conditions for identifiability and present algorithms for the infinite and finite sample settings, with associated performance guarantees. 

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