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A Bayesian model to estimate causality in PISA scores: a tutorial with application to ICT

Stefano Cabras and Juan de Dios Tena ()

DES - Working Papers. Statistics and Econometrics. WS from Universidad Carlos III de Madrid. Departamento de Estadística

Abstract: 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 contexts

Keywords: Propensity; score; Causality; in; education; BART; models (search for similar items in EconPapers)
Date: 2015-07
New Economics Papers: this item is included in nep-ict
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