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Identification and Estimation of Causal Mechanisms and Net Effects of a Treatment under Unconfoundedness

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Author Info

  • Flores, Carlos A.

    ()
    (University of Miami)

  • Flores-Lagunes, Alfonso

    ()
    (Binghamton University, New York)

Abstract

An important goal when analyzing the causal effect of a treatment on an outcome is to understand the mechanisms through which the treatment causally works. We define a causal mechanism effect of a treatment and the causal effect net of that mechanism using the potential outcomes framework. These effects provide an intuitive decomposition of the total effect that is useful for policy purposes. We offer identification conditions based on an unconfoundedness assumption to estimate them, within a heterogeneous effect environment, and for the cases of a randomly assigned treatment and when selection into the treatment is based on observables. Two empirical applications illustrate the concepts and methods.

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Bibliographic Info

Paper provided by Institute for the Study of Labor (IZA) in its series IZA Discussion Papers with number 4237.

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Length: 41 pages
Date of creation: Jun 2009
Date of revision:
Handle: RePEc:iza:izadps:dp4237

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Related research

Keywords: causal inference; causal mechanisms; post-treatment variables; principal stratification;

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References

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  1. Guido W. Imbens, 2004. "Nonparametric Estimation of Average Treatment Effects Under Exogeneity: A Review," The Review of Economics and Statistics, MIT Press, vol. 86(1), pages 4-29, February.
  2. Lorraine Dearden & Javier Ferri & Costas Meghir, 2002. "The Effect Of School Quality On Educational Attainment And Wages," The Review of Economics and Statistics, MIT Press, vol. 84(1), pages 1-20, February.
  3. Imbens, Guido W. & Wooldridge, Jeffrey M., 2008. "Recent Developments in the Econometrics of Program Evaluation," IZA Discussion Papers 3640, Institute for the Study of Labor (IZA).
  4. Bruce D. Meyer, 1995. "Lessons from the U.S. Unemployment Insurance Experiments," Journal of Economic Literature, American Economic Association, vol. 33(1), pages 91-131, March.
  5. Guido W. Imbens, 2010. "Better LATE Than Nothing: Some Comments on Deaton (2009) and Heckman and Urzua (2009)," Journal of Economic Literature, American Economic Association, vol. 48(2), pages 399-423, June.
  6. 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.
  7. Joshua D. Angrist & Guido W. Imbens, 1995. "Identification and Estimation of Local Average Treatment Effects," NBER Technical Working Papers 0118, National Bureau of Economic Research, Inc.
  8. Keisuke Hirano & Guido W. Imbens & Geert Ridder, 2003. "Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score," Econometrica, Econometric Society, vol. 71(4), pages 1161-1189, 07.
  9. Heckman, James J. & Lalonde, Robert J. & Smith, Jeffrey A., 1999. "The economics and econometrics of active labor market programs," Handbook of Labor Economics, in: O. Ashenfelter & D. Card (ed.), Handbook of Labor Economics, edition 1, volume 3, chapter 31, pages 1865-2097 Elsevier.
  10. Alberto Abadie & Guido W. Imbens, 2006. "Large Sample Properties of Matching Estimators for Average Treatment Effects," Econometrica, Econometric Society, vol. 74(1), pages 235-267, 01.
  11. Donald B. Rubin, 2005. "Causal Inference Using Potential Outcomes: Design, Modeling, Decisions," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 322-331, March.
  12. Heckman, James J & Ichimura, Hidehiko & Todd, Petra, 1998. "Matching as an Econometric Evaluation Estimator," Review of Economic Studies, Wiley Blackwell, vol. 65(2), pages 261-94, April.
  13. Lars Skipper & Marianne Simonsen, 2006. "The costs of motherhood: an analysis using matching estimators," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(7), pages 919-934.
  14. Mealli, Fabrizia & Rubin, Donald B., 2003. "Assumptions allowing the estimation of direct causal effects," Journal of Econometrics, Elsevier, vol. 112(1), pages 79-87, January.
  15. Manski, Charles F, 1990. "Nonparametric Bounds on Treatment Effects," American Economic Review, American Economic Association, vol. 80(2), pages 319-23, May.
  16. 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.
  17. Ronald G. Ehrenberg & George Jakubson & Jeffrey Groen & Eric So & Joseph Price, 2006. "Inside the Black Box of Doctoral Education: What Program Characteristics Influence Doctoral Students' Attrition and Graduation Probabilities?," NBER Working Papers 12065, National Bureau of Economic Research, Inc.
  18. Flores-Lagunes, Alfonso & Gonzalez, Arturo & Neumann, Todd C., 2005. "Learning but Not Earning? The Value of Job Corps Training for Hispanic Youths," IZA Discussion Papers 1638, Institute for the Study of Labor (IZA).
  19. Rajeev H. Dehejia & Sadek Wahba, 1998. "Causal Effects in Non-Experimental Studies: Re-Evaluating the Evaluation of Training Programs," NBER Working Papers 6586, National Bureau of Economic Research, Inc.
  20. Douglas Almond & Kenneth Y. Chay & David S. Lee, 2004. "The Costs of Low Birth Weight," NBER Working Papers 10552, National Bureau of Economic Research, Inc.
  21. 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.
  22. Heckman, James J & Smith, Jeffrey, 1997. "Making the Most Out of Programme Evaluations and Social Experiments: Accounting for Heterogeneity in Programme Impacts," Review of Economic Studies, Wiley Blackwell, vol. 64(4), pages 487-535, October.
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  24. Jinyong Hahn, 1998. "On the Role of the Propensity Score in Efficient Semiparametric Estimation of Average Treatment Effects," Econometrica, Econometric Society, vol. 66(2), pages 315-332, March.
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Citations

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Cited by:
  1. 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.
  2. Huber, Martin, 2014. "Causal pitfalls in the decomposition of wage gaps," Economics Working Paper Series 1405, University of St. Gallen, School of Economics and Political Science.
  3. Huber, Martin & Mellace, Giovanni & Lechner, Michael, 2014. "The finite sample performance of estimators for mediation analysis under sequential conditional independence," Economics Working Paper Series 1415, University of St. Gallen, School of Economics and Political Science.
  4. Huber, Martin & Mellace, Giovanni & Lechner, Michael, 2014. "Why do tougher caseworkers increase employment? The role of programme assignment as a causal mechanism," Economics Working Paper Series 1414, University of St. Gallen, School of Economics and Political Science.
  5. Sung Jae Jun & Joris Pinkse & Haiqing Xu & Nese Yildiz, 2012. "Identification of treatment effects in a triangular system of equations," Department of Economics Working Papers 130910, The University of Texas at Austin, Department of Economics, revised Oct 2012.
  6. 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.

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