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Outcome conditioned treatment effects

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  • Stefan Hoderlein

    ()
    (Institute for Fiscal Studies and Boston College)

  • Yuya Sasaki

Abstract

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.

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Bibliographic Info

Paper provided by Centre for Microdata Methods and Practice, Institute for Fiscal Studies in its series CeMMAP working papers with number CWP39/13.

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Date of creation: Aug 2013
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Handle: RePEc:ifs:cemmap:39/13

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Related research

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

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  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. Clément de Chaisemartin, 2012. "Late again, whithout Monotonicity," Working Papers 2012-12, Centre de Recherche en Economie et Statistique.
  3. 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.
  4. Stefan Hoderlein & Enno Mammen, 2007. "Identification of Marginal Effects in Nonseparable Models Without Monotonicity," Econometrica, Econometric Society, vol. 75(5), pages 1513-1518, 09.
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Cited by:
  1. Kasy, Maximilian, . "Instrumental variables with unrestricted heterogeneity and continuous treatment," Working Paper 33257, Harvard University OpenScholar.
  2. 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.

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