This paper presents a step-by-step tutorial to estimate causal effects in PISA 2012 by means of a
nonparametric Bayesian modeling approach known as Bayesian Additive Regression Trees
(BART), with an illustration of the causal impact of ICT on Spanish students' performance. The
R code is explained in a way that can be easily applied to other similar studies. The application
shows that, compared to more traditional methodologies, the BART approach is particularly
useful when a high-dimensional set of confounding variables is considered as its results are not
based on a sampling hypothesis. BART allows for the estimation of different interactive effects
between the treatment variable and other covariates. BART models do not require the analyst to
make explicit subjective decisions in which covariates must be included in the final models. This
makes it an easy procedure to guide policy makers' decisions in different contextsStefano Cabras has been supported by Ministerio de Ciencia e Innovaci on grant
MTM2013-42323, ECO2012-38442, RYC-2012-11455, by Ministero dell'Istruzione,
dell'Univesit a e della Ricerca of Italy and by Regione Autonoma della Sardegna
CRP-59903. Juan de Dios Tena Horrillo has been supported by Ministerio de Educación
y Ciencia, ECO2009-08100 and ECO2012-32401
