Causal inference involves determining whether a treatment (e.g., an education program) causes a change in outcomes (e.g., academic achievement). It is well-known that causal effects are more challenging to estimate than associations. Over the past 50 years, the potential outcomes framework has become one of the most widely used approaches for…

Machine learning (ML) methods for causal inference have gained popularity due to their flexibility to predict the outcome model and the propensity score. In this article, we provide a within-group approach for ML-based causal inference methods in order to robustly estimate average treatment effects in multilevel studies when there is cluster-level…

This paper investigates the main indicators of scores of K-12 leavers who were admitted at Princess Sumaya University for Technology, PSUT, in Jordan and their graduation scores. It uses time series data covering the period 1993-2012, including all 3,229 Bachelor graduates in all specialisations. The paper applies several statistical techniques to…

Children from low-income families are at risk of learning outcome difficulties, particularly in literacy. Various studies link poor literacy results with performance later in primary and secondary school, and suggest that poverty, literacy skills and weak instructional methods combine to drastically limit the educational opportunities for many…

Some form of a short interrupted time series (ITS) is often used to evaluate state and national programs. An ITS design with a single treatment group assumes that the pretest functional form can be validly estimated and extrapolated into the postintervention period where it provides a valid counterfactual. This assumption is problematic. Ambiguous…

This paper examines the estimation of two-stage clustered RCT designs in education research using the Neyman causal inference framework that underlies experiments. The key distinction between the considered causal models is whether potential treatment and control group outcomes are considered to be fixed for the study population (the…