Sample Selection in Randomized Trials With Multiple Target Populations
Bibliographic Data
| ID | 12470632 |
|---|---|
| Authors | Elise Tipton (0000-0001-5608-1282, Northwestern University, corresponding author) |
| Year | 2022 |
| Volume | 43 |
| Issue | 1 |
| Pages | 70-89 |
| Publication date | 2022-01-05 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | American Journal of Evaluation (JOURNAL) |
| Journal identifiers | ISSN: 1098-2140 • E-ISSN: 1557-0878 |
| Publisher | SAGE Publishing (PUBLISHER • US) |
| DOI | 10.1177/1098214020927787 |
| OpenAlex | W4206560621 |
| Language | EN |
| References cited | 18 |
Practitioners and policymakers often want estimates of the effect of an intervention for their local community, e.g., region, state, county. In the ideal, these multiple population average treatment effect (ATE) estimates will be considered in the design of a single randomized trial. Methods for sample selection for generalizing the sample ATE to date, however, focus only on the case of a single target population. In this paper, I provide a framework for sample selection in the multiple population case, including three compromise allocations. I situate the methods in an example and conclude with a discussion of the implications for the design of randomized evaluations more generally
Intervention (counseling · Machine learning · Population · Randomized controlled trial · Randomized experiment · Research design · Sample (material · Sample size determination · Selection (genetic algorithm · Selection bias · Sociology · Statistics · Advanced Causal Inference Techniques · Computer Science · Demography · Health Systems, Economic Evaluations, Quality of Life · Mathematics · Medicine · Psychology · Statistical Methods in Clinical Trials
| Citation velocity | historical |
|---|---|
| Highly cited | No |