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Nonparametric Partial Identification of Causal Net and Mechanism Average Treatment Effects

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  • Carlos A. Flores

    ()
    (Department of Economics, University of Miami)

  • Alfonso Flores-Lagunes

    (Department of Economics, University of Florida)

Abstract

When analyzing the causal e§ect of a treatment on an outcome it is important to un- derstand the mechanisms or channels through which the treatment works. In this paper we study net and mechanism average treatment e§ects (NATE and MATE, respectively), which provide an intuitive decomposition of the total average treatment e§ect (ATE) that enables learning about how the treatment a§ects the outcome. We derive informative non- parametric bounds for these two e§ects allowing for heterogeneous e§ects and without re- quiring the use of an instrumental variable or having an outcome with bounded support. We employ assumptions requiring weak monotonicity of mean potential outcomes within or across subpopulations deÖned by the potential values of the mechanism variable under each treatment arm. We illustrate the identifying power of our bounds by analyzing what part of the ATE of a training program on weekly earnings and employment is due to the obtainment of a GED, high school, or vocational degree.

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File URL: http://www.bus.miami.edu/_assets/files/faculty-and-research/academic-departments/eco/eco-working-papers/2010/wp-2010-25-Nonparametric-partial-identification-of-causal-net.pdf
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Bibliographic Info

Paper provided by University of Miami, Department of Economics in its series Working Papers with number 2010-25.

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Date of creation: Jul 2010
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Publication status: Forthcoming: Under review
Handle: RePEc:mia:wpaper:2010-25

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Related research

Keywords: causal inference; treatment effects; net effects; direct effects; nonparametric bounds; principal stratification;

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References

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  1. Alfonso Flores-Lagunes & Arturo Gonzalez & Todd Neumann, 2010. "Learning But Not Earning? The Impact Of Job Corps Training On Hispanic Youth," Economic Inquiry, Western Economic Association International, vol. 48(3), pages 651-667, 07.
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Cited by:
  1. Huber, Martin, 2012. "Identifying causal mechanisms in experiments (primarily) based on inverse probability weighting," Economics Working Paper Series 1213, University of St. Gallen, School of Economics and Political Science, revised May 2013.
  2. Bampasidou, Maria & Flores, Carlos A. & Flores-Lagunes, Alfonso, 2011. "Unbundling the Degree Effect in a Job Training Program for Disadvantaged Youth," 2011 Annual Meeting, July 24-26, 2011, Pittsburgh, Pennsylvania 103619, Agricultural and Applied Economics Association.
  3. Blanco, German & Flores, Carlos A. & Flores-Lagunes, Alfonso, 2011. "Bounds on Average and Quantile Treatment Effects of Job Corps Training on Wages," IZA Discussion Papers 6065, Institute for the Study of Labor (IZA).
  4. Huber, Martin & Mellace, Giovanni, 2011. "Testing instrument validity for LATE identification based on inequality moment constraints," Economics Working Paper Series 1143, University of St. Gallen, School of Economics and Political Science.

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