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Identification and Estimation of Casual Mechanisms and Net Effects of a Treatment

Author

Listed:
  • Carlos A. Flores

    () (Department of Economics, University of Miami)

  • Alfonso Flores-Lagunes

    () (Food and Resource Economics Department, University of Florida; Department of Economicsm, University of Arizona)

Abstract

An important goal in the analysis of the causal effect of a treatment on an outcome is to understand the mechanisms through which the treatment causally works. In the economics literature, however, there seems to be no available framework to estimate the relative importance of different causal mechanisms of a treatment. We fill this void by precisely defining a causal mechanism effect of a treatment and the causal effect of a treatment net of that mechanism using the potential outcomes framework, and by considering their identification and estimation. The definition of our parameters results in an intuitive decomposition of the total effect of a treatment that is useful for policy purposes. We offer conditions under which these causal effects can be estimated for the case of a randomly assigned treatment and when selection into the treatment is random conditional on a set of covariates. We close with two empirical applications that illustrate the concepts and methods introduced in this paper.

Suggested Citation

  • Carlos A. Flores & Alfonso Flores-Lagunes, 2007. "Identification and Estimation of Casual Mechanisms and Net Effects of a Treatment," Working Papers 0706, University of Miami, Department of Economics.
  • Handle: RePEc:mia:wpaper:0706
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    References listed on IDEAS

    as
    1. Manski, Charles F, 1990. "Nonparametric Bounds on Treatment Effects," American Economic Review, American Economic Association, vol. 80(2), pages 319-323, May.
    2. Donald B. Rubin, 2004. "Direct and Indirect Causal Effects via Potential Outcomes," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 31(2), pages 161-170.
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    5. Black, Dan A. & Smith, J.A.Jeffrey A., 2004. "How robust is the evidence on the effects of college quality? Evidence from matching," Journal of Econometrics, Elsevier, vol. 121(1-2), pages 99-124.
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    11. Currie, Janet & Neidell, Matthew, 2007. "Getting inside the "Black Box" of Head Start quality: What matters and what doesn't," Economics of Education Review, Elsevier, vol. 26(1), pages 83-99, February.
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    Citations

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    Cited by:

    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, July.
    2. Oscar A. Mitnik, 2007. "Intergenerational transmission of welfare dependency: The effects of length of exposure," Working Papers 0715, University of Miami, Department of Economics.
    3. 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.

    More about this item

    Keywords

    Casual inference; post-treatment variables; principal stratification.;

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General

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