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LATE with Missing or Mismeasured Treatment


  • Rossella Calvi

    (Rice University)

  • Arthur Lewbel

    () (Boston College)

  • Denni Tommasi

    () (Monash University)


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, 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.

Suggested Citation

  • Rossella Calvi & Arthur Lewbel & Denni Tommasi, 2018. "LATE with Missing or Mismeasured Treatment," Boston College Working Papers in Economics 959, Boston College Department of Economics, revised 15 Mar 2021.
  • Handle: RePEc:boc:bocoec:959
    Note: previously circulated as "Women’s Empowerment and Family Health: Estimating LATE with Mismeasured Treatment"

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    Cited by:

    1. Denni Tommasi & Lina Zhang, 2020. "Bounding Program Benefits When Participation is Misreported," Monash Econometrics and Business Statistics Working Papers 24/20, Monash University, Department of Econometrics and Business Statistics.
    2. Victor Hiller & Nouhoum Touré, 2020. "Endogenous Gender Power: The Two Facets of Empowerment," Working Papers 2020.04, International Network for Economic Research - INFER.
    3. Arthur Lewbel, 2019. "The Identification Zoo: Meanings of Identification in Econometrics," Journal of Economic Literature, American Economic Association, vol. 57(4), pages 835-903, December.
    4. Sung Jae Jun & Sokbae (Simon) Lee, 2018. "Identifying the effect of persuasion," CeMMAP working papers CWP19/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    5. Takahide Yanagi, 2019. "Inference on local average treatment effects for misclassified treatment," Econometric Reviews, Taylor & Francis Journals, vol. 38(8), pages 938-960, September.
    6. Klein, Matthew J. & Barham, Bradford L., 2018. "Point Estimates of Household Bargaining Power Using Outside Options," Staff Paper Series 590, University of Wisconsin, Agricultural and Applied Economics.
    7. Arthur Lewbel & Xirong Lin, 2019. "Identification of Semiparametric Model Coefficients, With an Application to Collective Households," Boston College Working Papers in Economics 1002, Boston College Department of Economics, revised 31 Jan 2021.
    8. Lina Zhang, 2020. "Spillovers of Program Benefits with Mismeasured Networks," Papers 2009.09614,

    More about this item


    LATE; missing treatment; measurement error; misclassification; collective model; resource shares; health;
    All these keywords.

    JEL classification:

    • D13 - Microeconomics - - Household Behavior - - - Household Production and Intrahouse Allocation
    • D11 - Microeconomics - - Household Behavior - - - Consumer Economics: Theory
    • D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis
    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • I32 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - Measurement and Analysis of Poverty

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