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
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Identifier |
https://doi.org/10.7910/DVN/UWYAJD
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Creator |
Lundberg, Ian
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Publisher |
Harvard Dataverse
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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.
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Subject |
Social Sciences
causal inference disparities stratification race class gender |
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Contributor |
Lundberg, Ian
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