Instrumental variables is a popular approach for causal inference in education when randomization of treatment is not feasible. Using a first-year college program as a running example, this article reviews the five assumptions that must be met to successfully use instrumental variables to estimate a causal effect with observational data: SUTVA,…

In the article "It's Just an Observation," Robinson and Wainer (Educational Psychology Review 35, Robinson, D., & Wainer, H. (2023). It's just an observation. Educational Psychology Review, 35(83), Published online: 14 August, 2023) lamented that educational psychology is moving toward the dark side of the quality continuum, with…

In the complex world of educational policy, causal inferences will be debated. As we review non-experimental designs in educational policy, we focus on how to clarify and focus the terms of debate. We begin by presenting the potential outcomes/counterfactual framework and then describe approximations to the counterfactual generated from the…

The central role of the propensity score analysis (PSA) in observational studies is for causal inference; as such, PSA is often used for making causal claims in research articles. However, there are still some issues for researchers to consider when making claims of causality using PSA results. This summary first briefly reviews PSA, followed by…

Experiments offer more reliable evidence on causation than observational studies, which is not to gainsay the contribution to knowledge from observation. Experiments should be analyzed as experiments, not as observational studies. A simple comparison of rates might be just the right tool, with little value added by "sophisticated" models. This…

D. B. Rubin's model for causal inference in experiments and observational studies is enlarged to analyze the problem of "causes causing causes" and is compared to path analysis and recursive structural equations models. A special quasiexperimental design, the encouragement design, is used to give concreteness to the discussion by…

Causal effect modeling with naturalistic rather than experimental data is challenging. In observational studies participants in different treatment conditions may also differ on pretreatment characteristics that influence outcomes. Propensity score methods can theoretically eliminate these confounds for all observed covariates, but accurate…

Studies evaluating hypotheses about sources of differential item functioning (DIF) are classified into two categories: observational studies evaluating operational items and randomized DIF studies evaluating specially constructed items. For observational studies, advice is given for item classification, sample selection, the matching criterion,…