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Late With Missing or Mismeasured Treatment

Bibliographic Data

ID19418552
AuthorsRossella Calvi (0000-0002-8259-4628, Rice University), Arthur Lewbel (0000-0003-3864-2618, Boston College, corresponding author), Denni Tommasi (0000-0003-4027-6047, University of Bologna)
Year2022
Volume40
Issue4
Pages1701-1717
Publication date2022-10-02
Peer ReviewedYes
Open AccessNo
TypeARTICLE
VenueJournal of Business and Economic Statistics (JOURNAL)
Journal identifiersISSN: 0735-0015 • E-ISSN: 1537-2707
PublisherInforma UK Limited (PUBLISHER • GB)
DOI10.1080/07350015.2021.1970573
OpenAlexW3194665345
LanguageEN
Citations received8
References cited73

We provide a new estimator, MR-LATE, that consistently estimates local average treatment effects when treatment is missing for some observations, not at random. If instead treatment is mismeasured for some observations, then MR-LATE usually has less bias than the standard LATE estimator. We discuss potential applications where an endogenous binary treatment may be unobserved or mismeasured. We apply MR-LATE to study the impact of women’s control over household resources on health outcomes in Indian families. This application illustrates the use of MR-LATE when treatment is estimated rather than observed. In these situations, treatment mismeasurement may arise from model misspecification and estimation errors

Econometrics · Economics · Estimation · Estimator · Missing data · Observational error · Statistics · Treatment effect · Gender, Labor, and Family Dynamics · Income, Poverty, and Inequality · Mathematics · Medicine · Poverty, Education, and Child Welfare

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Unique citing works8
Citations per year2,67
Citation span2023 - 2026 (4)
Citation velocitycurrent
Highly citedNo
Citation typesNeutral: 7

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