The Impact of Attending a Remedial Support Program on Syrian Children's Reading Skills: Using BART for Causal Inference

TL;DR

This study evaluates the causal impact of a remedial support program with social-emotional learning on Syrian refugee children's reading skills using advanced machine learning and Bayesian methods, revealing mixed effects across different assessments.

Contribution

It introduces a novel application of BART for causal inference in an understudied refugee education context, addressing self-selection and measurement challenges.

Findings

Positive effects on three reading measures from Arabic EGRA assessment

No conclusive evidence on the primary ASER reading measure

Highlights importance of measurement and data collection choices

Abstract

This article estimates, for a sample of 1,777 Syrian refugee children, the impact on basic reading assessments of attending a remedial support program in Lebanon that was infused with social and emotional learning practices. We use flexible methods that capitalize on advantages of both machine learning and Bayesian inferential frameworks to leverage the information available in understudied contexts and help account for the problem of self-selection. Average treatment effects were estimated both using multiply imputed data and data from outcome-respondents only. We do not find conclusive evidence for an effect on one of the reading measures studied (ASER). However, we provide evidence for positive effects for three, more robust, measures of basic reading outcomes from the Arabic EGRA assessment. We discuss potential reasons for the differences in effects that are relevant for educational research and practice. We also consider the implications for future research of choices related to measurement, data collection and processing, and missing data.