Personalized learning considers that the causal effects of a studied learning intervention may differ for the individual student. Making the inference about causal effects of studies interventions is a central problem. In this paper we propose the Residual Counterfactual Networks (RCN) for answering counterfactual inference questions, such as…

This report describes in detail how researchers, policymakers, and program administrators can recognize opportunities for experiments and carry them out. Specifically, the report focuses on opportunistic experiments, defined as randomized controlled trials (RCTs) that study the effects of initiatives, program changes, or policy actions that…

Questions regarding the generalizability of results from educational experiments have been at the forefront of methods development over the past five years. This work has focused on methods for estimating the effect of an intervention in a well-defined inference population (e.g., Tipton, 2013; O'Muircheartaigh and Hedges, 2014); methods for…

Randomized experiments are commonly used to evaluate if particular interventions improve student achievement. While these experiments can establish that a treatment actually "causes" changes, typically the participants are not randomly selected from a well-defined population and therefore the results do not readily generalize. Three…