Background: Clustered randomized controlled trials are commonly used to evaluate the effectiveness of treatments. Frequently, stratified or paired designs are adopted in practice. Fogarty (2018) studied variance estimators for stratified and not clustered experiments and Schochet et. al. (2022) studied that for stratified, clustered RCTs with…

A linear model is often used to find the effect of a binary treatment D on a noncontinuous outcome Y with covariates X. Particularly, a binary Y gives the popular "linear probability model (LPM)," but the linear model is untenable if X contains a continuous regressor. This raises the question: what kind of treatment effect does the…

When planning a cluster randomized trial, evaluators often have access to an enumerated cohort representing the target population of clusters. Practicalities of conducting the trial, such as the need to oversample clusters with certain characteristics in order to improve trial economy or support inferences about subgroups of clusters, may preclude…

This article reviews important statistical methods for estimating the impact of interventions on outcomes in education settings, particularly programs that are implemented in field, rather than laboratory, settings. We begin by describing the causal inference challenge for evaluating program effects. Then four research designs are discussed that…

Randomized controlled trials (RCTs) and quasi-experimental designs like regression discontinuity (RD) designs, instrumental variable (IV) designs, and matching and propensity score (PS) designs are frequently used for inferring causal effects. It is well known that the features of these designs facilitate the identification of a causal estimand…

Experimental and quasi-experimental designs play a central role in estimating cause-effect relationships in education, psychology, and many other fields of the social and behavioral sciences. This paper presents and discusses the causal graphs of experimental and quasi-experimental designs. For quasi-experimental designs the authors demonstrate…

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…