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UID:event-10439@www.amse-aixmarseille.fr
DTSTAMP:20260422T015114Z
CREATED:20260422T015114Z
LAST-MODIFIED:20260422T015114Z
STATUS:CONFIRMED
SEQUENCE:0
SUMMARY:big data and econometrics seminar - Guillaume Hollard
DTSTART:20240213T130000Z
DTEND:20240213T143000Z
DESCRIPTION:In this paper\, we introduce a novel randomization procedure fo
 r randomized controlled trials (RCTs) designed to improve the utilization o
 f baseline information. We start by offering an overview of prevailing meth
 ods employed for unit allocation to treatment. Our investigation reveals a 
 prevalent under-utilization of baseline information by empiricists. Indeed\
 , baseline information is collected before randomization takes places in 90
 % of RCT published in top-5 journals over the last five years. However\, th
 is crucial information is used in only half of the papers incorporate this 
 information for covariate balancing in the randomization process.The most p
 opular methods (e.g. stratification and pairwise matching) do not ensure p
 erfect balance\, especially when dealing with continuous and/or numerous co
 variates (e.g.\, stratification and pairwise matching). Other methods\, suc
 h as rerandomization\, limit the scope for robust inference. We here adapt 
 a sampling algorithm\, named the cube method the Cube method (Deville and T
 illé 2004) and show how it can be used to overcome identifying limitations
  of existing randomization methods. The Cube method enables the selection o
 f perfectly balanced samples for any covariate\, whether continuous or cate
 gorical\, ensuring consistent adherence to balance tests. Moreover\, the me
 thod facilitates the generation of unambiguous confidence intervals and yie
 lds substantial gains in precision\, particularly when covariates exhibit c
 orrelations with potential outcomes. The Cube method further allows for the
  flexible determination of assignment probabilities\, permitting variation 
 across subgroups (e.g.\, to address sample attrition concerns). We provide
  comprehensive theoretical insights and conduct simulation exercises using 
 both randomly generated and real data to illustrate the overarching advanta
 ges of the Cube method. Our findings support the contention that the Cube m
 ethod represents a robust and versatile approach to address the challenges 
 associated with covariate balancing in RCTs\, offering researchers an effec
 tive tool for experimental design.\\n\\nContact: Michel Lubrano : michel.lu
 brano[at]univ-amu.frPierre Michel : pierre.michel[at]univ-amu.fr\n\nPlus d
 'informations: https://www.amse-aixmarseille.fr/fr/evenements/guillaume-hol
 lard-3
LOCATION:Îlot Bernard du Bois - Salle 21\, AMU - AMSE\, 5-9 boulevard Maur
 ice Bourdet\, 13001 Marseille
URL;VALUE=URI:https://www.amse-aixmarseille.fr/fr/evenements/guillaume-hollard-3
CONTACT:Michel Lubrano : michel.lubrano[at]univ-amu.frPierre Michel :&nbsp\
 ;pierre.michel[at]univ-amu.fr
TRANSP:OPAQUE
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