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DTSTART;TZID=Europe/Paris:20260129T120000
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DTSTAMP:20260121T131625Z
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UID:25625-1769688000-1769692500@www.bse.u-bordeaux.fr
SUMMARY:Séminaire: Pedro Torres (LSE) 
DESCRIPTION:Séminaire HOPE / BxSE\n  \nPedro Torres\nLSE  \n  \nQuantile Adjustment: Bias reduction in income imputation when external covariates matter\n \nAbstract: \nEmpirical research often relies on combining information from multiple datasets when key variables are not jointly observed. A widely used solution is the two-sample two-stage (TSTS) procedure\, which imputes missing variables from a donor dataset and then estimates relationships using the imputed values. While TSTS enables otherwise infeasible analyses\, it introduces two sources of bias: variance bias\, due to reduced variability in imputed variables\, and projection bias\, stemming from Berkson measurement error. These biases affect estimands differently depending on whether the imputed variable is used as a regressor or as an outcome. Existing correction methods typically address only one source of bias—either restoring variance through stochastic imputation or correcting projection bias via rescaling—leaving the other unresolved. This paper proposes a novel quantile-based imputation approach that simultaneously targets both biases by adjusting first-stage predictions at the quantile level. The method fully restores the variance of the imputed variable and partially recovers the covariance lost during imputation\, thereby improving the consistency of second-stage estimates.\n \n \nAbout the author: Pedro holds a BSc in Economics from the Universidad Iberoamericana and an MSc in Applied Social Data Science from the London School of Economics. His PhD focuses on inequalities\, with a particular emphasis on the intergenerational transmission of inequalities and statistical methods for its analysis.\nHis current research focuses on methodological aspects of analysing the intergenerational transmission of inequalities\, particularly through the use of machine learning. In addition\, he has focused on the analysis of inequalities and their impact on the Mexican population.\n \n \n  \n\n \nAgenda > Tous les événements\n\n \n  \n  \n 
URL:https://www.bse.u-bordeaux.fr/agenda/seminaire-pedro-torres-lse/
LOCATION:BxSE\, salle de séminaire H2-116\, bât.H\, campus Pessac\, avenue Leon Duguit\, Pessac\, 33600
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DTSTART;TZID=Europe/Paris:20260129T170000
DTEND;TZID=Europe/Paris:20260129T183000
DTSTAMP:20260601T162508Z
CREATED:20260123T140701Z
LAST-MODIFIED:20260601T162508Z
UID:25647-1769706000-1769711400@www.bse.u-bordeaux.fr
SUMMARY:Impact de l'IA générative sur l'apprentissage - Atelier
DESCRIPTION:Atelier\n Impact de l’IA générative sur l’apprentissage\n  \nJeudi 29 janvier\, de 17h à 18h30BU station Marne — 16-22 cours de la Marne – Bordeaux (Etage 1 – salle 108)\n  \nAprès « Explorer la littérature scientifique avec l’IA »\, « L’IA dans les sciences juridiques« \, « Formuler des prompts efficaces et pertinents »et « LLM et SHS«  \n–> ce mois-ci  : Impact de l’IA générative sur l’apprentissage \n  \nDiscutants : \nNicolas Charles et Mickael Temporão (Enseignants-Chercheurs au CED) \nUne expérimentation menée auprès d’étudiants de droit compare 3 situations d’apprentissage : sans IA\, avec une IA classique et avec une IA jouant le rôle de tuteur académique. Les étudiants devaient synthétiser un texte juridique\, puis restituer ses idées 2 semaines plus tard sans support.\n\n+ boisson offerte\n+ courte pause active – relâcher les tensions\, booster son énergie\n \n \nUn évènement porté par le département CHANGES\, le GIS URFIST\, BxSE et la PUD de Bordeaux\n\n  \nComité d’organisation : Claire Kersuzan (PUD Bx) & Olha Nahorma (BxSE) & Karine Onfroy (BxSE) \n \n\n\n\n \nAgenda > Tous les événements\n\n \n  \n  \n 
URL:https://www.bse.u-bordeaux.fr/agenda/impact-de-lia-generative-sur-lapprentissage-atelier/
LOCATION:BU station Marne — 16-22 cours de la Marne – Bordeaux (Etage 1 – salle 108)
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