As evidence from evaluation and experimental studies continue to influence decision and policymaking, applied researchers and practitioners require tools to derive valid and credible inferences. Over the past several decades, research in causal inference has progressed with the development and application of propensity scores. Since their…

The causal view of categories assumes that categories are represented by features and their causal relations. To study the effect of causal knowledge on categorization, researchers have used Bayesian causal models. Within that framework, categorization may be viewed as dependent on a likelihood computation (i.e., the likelihood of an exemplar with…

In educational contexts, students often self-select into specific interventions (e.g., courses, majors, extracurricular programming). When students self-select into an intervention, systematic group differences may impact the validity of inferences made regarding the effect of the intervention. Propensity score methods are commonly used to reduce…

Student success and drop-out predictions have gained increased attention in recent years, connected to the hope that by identifying struggling students, it is possible to intervene and provide early help and design programs based on patterns discovered by the models. Though by now many models exist achieving remarkable accuracy-values, models…

Recent literature has underscored the critical role of treatment effect variation in estimating and understanding causal effects. This approach, however, is in contrast to much of the foundational research on causal inference. Linear models, for example, classically rely on constant treatment effect assumptions, or treatment effects defined by…

Recent methods to improve generalizations from nonrandom samples typically invoke assumptions such as the strong ignorability of sample selection, which is challenging to meet in practice. Although researchers acknowledge the difficulty in meeting this assumption, point estimates are still provided and used without considering alternative…

When randomized experiments are infeasible, quasi-experimental designs can be exploited to evaluate causal treatment effects. The strongest quasi-experimental designs for causal inference are regression discontinuity designs, instrumental variable designs, matching and propensity score designs, and comparative interrupted time series designs. This…

Research has documented two effects of interfeature causal knowledge on classification. A "causal status effect" occurs when features that are causes are more important to category membership than their effects. A "coherence effect" occurs when combinations of features that are consistent with causal laws provide additional…

The IADIS CELDA 2012 Conference intention was to address the main issues concerned with evolving learning processes and supporting pedagogies and applications in the digital age. There had been advances in both cognitive psychology and computing that have affected the educational arena. The convergence of these two disciplines is increasing at a…