The Gap-Closing Estimand: A Causal Approach to Study Interventions That Close Disparities Across Social Categories

Ian Lundberg

Sociological Methods & Research, 2024, vol. 53, issue 2, 507-570

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.

Keywords: causal inference; disparities; stratification; race; class; gender (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:sae:somere:v:53:y:2024:i:2:p:507-570

DOI: 10.1177/00491241211055769

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