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

Harvard Dataverse (Africa Rice Center, Bioversity International, CCAFS, CIAT, IFPRI, IRRI and WorldFish)

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Title Replication Code: The Gap-Closing Estimand: A Causal Approach to Study Interventions That Close Disparities Across Social Categories
 
Identifier https://doi.org/10.7910/DVN/UWYAJD
 
Creator Lundberg, Ian
 
Publisher Harvard Dataverse
 
Description 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 produce results that may directly inform policy: they tell us the degree to which an intervention applied to a sample would close a gap. I provide open-source software (the R package gapclosing) to support these methods.
 
Subject Social Sciences
causal inference
disparities
stratification
race
class
gender
 
Contributor Lundberg, Ian