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.

CALVI R, LEWBEL A, TOMMASI D (2022). LATE with Missing or Mismeasured Treatment. JOURNAL OF BUSINESS & ECONOMIC STATISTICS, 40(4), 1701-1717 [10.1080/07350015.2021.1970573].

LATE with Missing or Mismeasured Treatment

TOMMASI D
2022

Abstract

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.
2022
CALVI R, LEWBEL A, TOMMASI D (2022). LATE with Missing or Mismeasured Treatment. JOURNAL OF BUSINESS & ECONOMIC STATISTICS, 40(4), 1701-1717 [10.1080/07350015.2021.1970573].
CALVI R; LEWBEL A; TOMMASI D
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/860933
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