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DTSTART;TZID=Europe/Helsinki:20220629T140000
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SUMMARY:Tobias Wolfram (University of Bielefeld\, ENSAE) "Man - Machine - Gene: Predicting (non)cognitive ability and educational attainment from nonstandard data"
DESCRIPTION:Statistics-Econometrics-Machine Learning \nTime: 2:00 pm -3.00pm\nDate: 29th of June 2022\nZoom \nTobias Wolfram (University of Bielefeld\, ENSAE) “Man – Machine – Gene: Predicting (non)cognitive ability and educational attainment from nonstandard data” \nAbstract:To what extent can nonstandard types of data predict psychological and social outcomes? We leverage a unique British dataset to study the predictive utility of short essays written at age 11 and genetic polymorphisms. Using state-of-the-art methods from natural language processing and genomics\, we find that both approaches predict cognitive ability\, non-cognitive traits and educational attainment with in part impressive precision: Performance based on the text samples (up to 61\, 9 and 25%) mirrors that of teacher evaluations (up to 66\, 19\, 29%) obtained at the same age. Prediction from genetic data is overall substantial\, but measurably smaller (up to 17\, 5\, 19%). Combining all three sources of data explains 38% of variation in educational attainment and 70% in cognitive ability\, approaching test-retest reliability of benchmark intelligence tests. We conclude that in order to improve predictive performance in the social and behavioral sciences\, more attention should be paid to nonstandard data sources. \n  \nOrganizers:\nCristina BUTUCEA (CREST)\, Alexandre TSYBAKOV (CREST)\, Karim LOUNICI (CMAP) \, Jaouad MOURTADA (CREST)\nSponsors:\nCREST-CMAP \n
URL:https://crest.science/event/tobias-wolfram-university-of-bielefeld-ensae-man-machine-gene-predicting-noncognitive-ability-and-educational-attainment-from-nonstandard-data/
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