Researchers would often like to leverage data from a collection of sources (e.g., meta-analyses of randomized trials, multi-center trials, pooled analyses of observational cohorts) to estimate causal effects in a target population of interest. However, because different data sources typically represent different underlying populations, traditional meta-analytic methods may not produce causally interpretable estimates that apply to any reasonable target population. In this article, we present the CausalMetaR R package, which implements robust and efficient methods to estimate causal effects in a given internal or external target population using multi-source data. The package includes estimators of average and subgroup treatment effects for the entire target population. To produce efficient and robust estimates of causal effects, the package implements doubly robust and non-parametric efficient estimators and supports using flexible data-adaptive (e.g., machine learning techniques) methods and cross-fitting techniques to estimate the nuisance models (e.g., the treatment model, the outcome model). We briefly review the methods, describe the key features of the package, and demonstrate how to use the package through an example. The package aims to facilitate causal analyses in the context of meta-analysis.

We examine the generalizability of single-topic studies, focusing on how often their confidence intervals capture the typical treatment effect from a larger population of possible studies. We show that the confidence intervals from these single-topic studies capture the typical effect from a population of topics at well below the nominal rate. For a plausible scenario, the confidence interval from a single-topic study might only be half as wide as an interval that captures the typical effect at the nominal rate. We highlight three important conclusions. First, we emphasize that researchers and readers must take care when generalizing the inferences from single-topic studies to a larger population of possible studies. Second, we demonstrate the critical importance of similarity across topics in drawing inferences and encourage researchers to consider designs that explicitly estimate and leverage similarity. Third, we emphasize that, despite their limitations, single-topic experiments have some important advantages.

Optimal treatment regimes (OTRs) have been widely employed in computer science and personalized medicine to provide data-driven, optimal recommendations to individuals. However, previous research on OTRs has primarily focused on settings that are independent and identically distributed, with little attention given to the unique characteristics of educational settings, where students are nested within schools and there are hierarchical dependencies. The goal of this study is to propose a framework for designing OTRs from multisite randomized trials, a commonly used experimental design in education and psychology to evaluate educational programs. We investigate modifications to popular OTR methods, specifically Q-learning and weighting methods, in order to improve their performance in multisite randomized trials. A total of 12 modifications, 6 for Q-learning and 6 for weighting, are proposed by utilizing different multilevel models, moderators, and augmentations. Simulation studies reveal that all Q-learning modifications improve performance in multisite randomized trials and the modifications that incorporate random treatment effects show the most promise in handling cluster-level moderators. Among weighting methods, the modification that incorporates cluster dummies into moderator variables and augmentation terms performs best across simulation conditions. The proposed modifications are demonstrated through an application to estimate an OTR of conditional cash transfer programs using a multisite randomized trial in Colombia to maximize educational attainment.

When researchers design an experiment, they usually hold potentially relevant features of the experiment constant. We call these details the “topic” of the experiment. For example, researchers studying the impact of party cues on attitudes must inform respondents of the parties’ positions on a particular policy. In doing so, researchers implement just one of many possible designs . Clifford, Leeper, and Rainey (2023. “Generalizing Survey Experiments Using Topic Sampling: An Application to Party Cues.” Forthcoming in Political Behavior. https://doi.org/10.1007/s11109-023-09870-1) argue that researchers should implement many of the possible designs in parallel—what they call “topic sampling”—to generalize to a larger population of topics. We describe two estimators for topic-sampling designs: First, we describe a nonparametric estimator of the typical effect that is unbiased under the assumptions of the design; and second, we describe a hierarchical model that researchers can use to describe the heterogeneity. We suggest describing the heterogeneity across topics in three ways: (1) the standard deviation in treatment effects across topics, (2) the treatment effects for particular topics, and (3) how the treatment effects for particular topics vary with topic-level predictors. We evaluate the performance of the hierarchical model using the Strengthening Democracy Challenge megastudy and show that the hierarchical model works well.

The success of protests depends on whether they favorably affect public opinion: nonviolent resistance can win public support for a movement, but regimes counter by framing protest as violent and instigated by outsiders. The authors argue that public perceptions of whether a protest is violent shift based on the framing of the types of action and the identities of participants in those actions. The article distinguishes between three dimensions: (1) threat of harm, (2) bearing of arms and (3) identity of protesters. Using survey experiments in Israel and the United States, the study finds support for framing effects. Threat of harm has the largest positive effect on perceptions of violence and support for repression. Surprisingly, social out-groups are not perceived as more violent, but respondents favor repressing them anyway. Support for repressing a nonthreatening out-group is at least as large as support for repressing a threatening in-group. The findings link contentious action and public opinion, and demonstrate the susceptibility of this link to framing.

Traditionally, benefit-cost analyses focus on average benefits and average costs. However, heterogeneous treatment effects and/or costs are most often present, which means that there is an efficiency potential hidden in the implementation of public programs, if policies can be targeted at those who, net of costs, benefit the most. We introduce efficiency potential defined as the ratio between the net benefit achieved under perfect selection of the individuals with significant positive net benefit of program participation, and the actually realized benefits net of costs. Using data from a randomized control-trial experiment of a Danish return-to-work program combined with rich administrative records and survey data, we find that there is indeed a potential for increased efficiency. Results from the treatment literature indicate that, generally, it may be difficult to harvest the full potential. Our application corroborates this finding.