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Outcome conditioned treatment effects


  • Stefan Hoderlein

    () (Institute for Fiscal Studies and Boston College)

  • Yuya Sasaki

    (Institute for Fiscal Studies)


This paper introduces average treatment effects conditional on the outcomes variable in an endogenous setup where outcome Y, treatment X and instrument Z are continuous. These objects allow to refine well studied treatment effects like ATE and ATT in the case of continuous treatment (see Florens et al (2009)), by breaking them up according to the rank of the outcome distribution. For instance, in the returns to schooling case, the outcome conditioned average treatment effect on the treated (ATTO), gives the average effect of a small increase in schooling on the subpopulation characterised by a certain treatment intensity, say 16 years of schooling, and a certain rank in the wage distribution. We show that IV type approaches are better suited to identify overall averages across the population like the average partial effect, or outcome conditioned versions thereof, while selection type methods are better suited to identify ATT or ATTO. Importantly, none of the identification relies on rectangular support of the errors in the identification equation. Finally, we apply all concepts to analyse the nonlinear heterogeneous effects of smoking during pregnancy on infant birth weight.

Suggested Citation

  • Stefan Hoderlein & Yuya Sasaki, 2013. "Outcome conditioned treatment effects," CeMMAP working papers CWP39/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  • Handle: RePEc:ifs:cemmap:39/13

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    References listed on IDEAS

    1. Jun, Sung Jae & Pinkse, Joris & Xu, Haiqing, 2011. "Tighter bounds in triangular systems," Journal of Econometrics, Elsevier, vol. 161(2), pages 122-128, April.
    2. Stefan Hoderlein & Enno Mammen, 2007. "Identification of Marginal Effects in Nonseparable Models Without Monotonicity," Econometrica, Econometric Society, vol. 75(5), pages 1513-1518, September.
    3. Clément de Chaisemartin, 2012. "Late again, whithout Monotonicity," Working Papers 2012-12, Center for Research in Economics and Statistics.
    4. Heckman, James J. & Robb, Richard Jr., 1985. "Alternative methods for evaluating the impact of interventions : An overview," Journal of Econometrics, Elsevier, vol. 30(1-2), pages 239-267.
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    Cited by:

    1. Xavier d'Haultfoeuille & Stefan Hoderlein & Yuya Sasaki, 2013. "Nonlinear difference-in-differences in repeated cross sections with continuous treatments," CeMMAP working papers CWP40/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    2. Kasy, Maximilian, "undated". "Instrumental variables with unrestricted heterogeneity and continuous treatment - DON'T CITE! SEE ERRATUM BELOW," Working Paper 33257, Harvard University OpenScholar.
    3. Maximilian Kasy, 2014. "Instrumental Variables with Unrestricted Heterogeneity and Continuous Treatment," Review of Economic Studies, Oxford University Press, vol. 81(4), pages 1614-1636.

    More about this item


    Continuous treatment; average treatment effect on the treated; marginal treatment effect; average partial effect; local instrumental variables; nonseparable model; endogeneity; quantiles;

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C26 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Instrumental Variables (IV) Estimation

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