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

Author

Listed:
  • Flores, Carlos A.

    () (California Polytechnic State University)

  • Flores-Lagunes, Alfonso

    () (Syracuse University)

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.

Suggested Citation

  • Flores, Carlos A. & Flores-Lagunes, Alfonso, 2009. "Identification and Estimation of Causal Mechanisms and Net Effects of a Treatment under Unconfoundedness," IZA Discussion Papers 4237, Institute for the Study of Labor (IZA).
  • Handle: RePEc:iza:izadps:dp4237
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    References listed on IDEAS

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    Cited by:

    1. Huber, Martin & Steinmayr, Andreas, 2017. "A Framework for Separating Individual Treatment Effects From Spillover, Interaction, and General Equilibrium Effects," Rationality and Competition Discussion Paper Series 21, CRC TRR 190 Rationality and Competition.
    2. Martin Huber & Michael Lechner & Anthony Strittmatter, 2018. "Direct and indirect effects of training vouchers for the unemployed," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 181(2), pages 441-463, February.
    3. Markus Frölich & Martin Huber, 2017. "Direct and indirect treatment effects–causal chains and mediation analysis with instrumental variables," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(5), pages 1645-1666, November.
    4. Martin Huber & Michael Lechner & Giovanni Mellace, 2016. "The Finite Sample Performance of Estimators for Mediation Analysis Under Sequential Conditional Independence," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(1), pages 139-160, January.
    5. repec:tpr:restat:v:99:y:2017:i:1:p:180-183 is not listed on IDEAS
    6. repec:eee:ecoedu:v:59:y:2017:i:c:p:63-80 is not listed on IDEAS
    7. Mofya-Mukuka, Rhoda & Kuhlgatz, Christian, 2014. "Nutritional Effects of Agricultural Diversification and Commercialization in Children in Zambia," 2014 Annual Meeting, July 27-29, 2014, Minneapolis, Minnesota 170506, Agricultural and Applied Economics Association.
    8. Sarah Baird & Jacobus de Hoop & Berk Özler, 2013. "Income Shocks and Adolescent Mental Health," Journal of Human Resources, University of Wisconsin Press, vol. 48(2), pages 370-403.
    9. Eva Deuchert & Martin Huber & Mark Schelker, 2016. "Direct and Indirect Effects Based on Difference-in-Differences with an Application to Political Preferences Following the Vietnam Draft Lottery," CESifo Working Paper Series 6000, CESifo Group Munich.
    10. Martin Huber, 2015. "Causal Pitfalls in the Decomposition of Wage Gaps," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 33(2), pages 179-191, April.
    11. Conny Wunsch & Renate Strobl, 2018. "Identification of Causal Mechanisms Based on Between-Subject Double Randomization Design," CESifo Working Paper Series 7142, CESifo Group Munich.
    12. Martin Huber, 2016. "Disentangling policy effects into causal channels," IZA World of Labor, Institute for the Study of Labor (IZA), pages 259-259, May.
    13. repec:eee:wdevel:v:108:y:2018:i:c:p:28-46 is not listed on IDEAS
    14. Wunsch, Conny & Strobl, Renate, 2018. "Identification of Causal Mechanisms Based on Between-Subject Double Randomization Designs," IZA Discussion Papers 11626, Institute for the Study of Labor (IZA).
    15. 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.
    16. Martin Huber & Michael Lechner & Giovanni Mellace, 2017. "Why Do Tougher Caseworkers Increase Employment? The Role of Program Assignment as a Causal Mechanism," The Review of Economics and Statistics, MIT Press, vol. 99(1), pages 180-183, March.
    17. 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.
    18. Luna Bellani & Michela Bia, 2016. "Intergenerational poverty transmission in Europe: The role of education," Working Paper Series of the Department of Economics, University of Konstanz 2016-02, Department of Economics, University of Konstanz.
    19. repec:taf:jnlasa:v:111:y:2016:i:514:p:510-525 is not listed on IDEAS
    20. 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.
    21. Bellani, Luna & Bia, Michela, 2017. "The Long-Run Impact of Childhood Poverty and the Mediating Role of Education," IZA Discussion Papers 10677, Institute for the Study of Labor (IZA).
    22. Cisneros, Elias & Zhou, Sophie & Borner, Jan, 2015. "Forest Law enforcement through district blacklisting in the Brazlian Amazon," 2015 Conference, August 9-14, 2015, Milan, Italy 211547, International Association of Agricultural Economists.

    More about this item

    Keywords

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

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: 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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