RT Generic
T1 A Bayesian model to estimate causality in PISA scores: a tutorial with application to ICT
A1 Cabras, Stefano
A1 Tena Horrillo, Juan de Dios
A2 Universidad Carlos III de Madrid. Departamento de Estadística, 
AB This paper presents a step-by-step tutorial to estimate causal effects in PISA 2012 by means of anonparametric Bayesian modeling approach known as Bayesian Additive Regression Trees(BART), with an illustration of the causal impact of ICT on Spanish students' performance. TheR code is explained in a way that can be easily applied to other similar studies. The applicationshows that, compared to more traditional methodologies, the BART approach is particularlyuseful when a high-dimensional set of confounding variables is considered as its results are notbased on a sampling hypothesis. BART allows for the estimation of different interactive effectsbetween the treatment variable and other covariates. BART models do not require the analyst tomake explicit subjective decisions in which covariates must be included in the final models. Thismakes it an easy procedure to guide policy makers' decisions in different contexts
SN 2387-0303
YR 2015
FD 2015-07
LK https://hdl.handle.net/10016/21456
UL https://hdl.handle.net/10016/21456
LA eng
NO Stefano Cabras has been supported by Ministerio de Ciencia e Innovaci on grantMTM2013-42323, ECO2012-38442, RYC-2012-11455, by Ministero dell'Istruzione,dell'Univesit a e della Ricerca of Italy and by Regione Autonoma della SardegnaCRP-59903. Juan de Dios Tena Horrillo has been supported by Ministerio de Educacióny Ciencia, ECO2009-08100 and ECO2012-32401.
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RD 4 may. 2024