Collaborative Research: Aggregating Evidence for the Analysis of Experiments

NSF Award Search · 01002526DB NSF RESEARCH & RELATED ACTIVIT · $174,874 · view on nsf.gov ↗

Abstract

This award funds research to develop a new methodology to distinguish between research findings that can be generalized across populations, places, and time, and those that cannot be generalized. This research addresses a fundamental challenge in empirical research: determining which experimental or observational findings are generalizable across different environments and populations. Existing methods do this by using restrictive assumptions, potentially leading to false generalization. By introducing a new methodology to detect generalizability, this research helps identify features of the environment, population characteristics, and treatment conditions that systematically contribute to generalizable results and those that exhibit context-specific or unpredictable results, and therefore not generalizable. The research results improve the reliability of evidence-based decision recommendations and the quality of decision design. By offering a rigorous approach to identify generalizability, this research makes significant contributions to economics science and beneficially informs decision makers and practitioners. The results of this research aid improved decision making, speed up economic growth, and hence improve living standards. This award funds a research agenda that develops a new methodology to distinguish between research results that are generalizable and those that are not. Methodologically, the research advances statistical meta-analysis by developing es

Key facts

NSF award ID
2447089
Awardee
Stanford University (CA)
SAM.gov UEI
HJD6G4D6TJY5
PI
Arun G Chandrasekhar
Primary program
01002526DB NSF RESEARCH & RELATED ACTIVIT
All programs
Estimated total
$174,874
Funds obligated
$174,874
Transaction type
Standard Grant
Period
07/01/2025 → 06/30/2027