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The Gap-Closing Estimand: A Causal Approach to Study Interventions That Close Disparities Across Social Categories.
- Source :
- Sociological Methods & Research; May2024, Vol. 53 Issue 2, p507-570, 64p
- Publication Year :
- 2024
-
Abstract
- Disparities across race, gender, and class are important targets of descriptive research. But rather than only describe disparities, research would ideally inform interventions to close those gaps. The gap-closing estimand quantifies how much a gap (e.g., incomes by race) would close if we intervened to equalize a treatment (e.g., access to college). Drawing on causal decomposition analyses, this type of research question yields several benefits. First, gap-closing estimands place categories like race in a causal framework without making them play the role of the treatment (which is philosophically fraught for non-manipulable variables). Second, gap-closing estimands empower researchers to study disparities using new statistical and machine learning estimators designed for causal effects. Third, gap-closing estimands can directly inform policy: if we sampled from the population and actually changed treatment assignments, how much could we close gaps in outcomes? I provide open-source software (the R package gapclosing) to support these methods. [ABSTRACT FROM AUTHOR]
- Subjects :
- RACE
STATISTICAL learning
RESEARCH questions
MACHINE learning
RESEARCH personnel
Subjects
Details
- Language :
- English
- ISSN :
- 00491241
- Volume :
- 53
- Issue :
- 2
- Database :
- Complementary Index
- Journal :
- Sociological Methods & Research
- Publication Type :
- Academic Journal
- Accession number :
- 176861664
- Full Text :
- https://doi.org/10.1177/00491241211055769