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Identification of the effects of dynamic treatments by sequential conditional independence assumptions

  • Michael Lechner

    ()

  • Ruth Miquel

This paper approaches the causal analysis of sequences of interventions from a potential outcome perspective. The identifying power of several different assumptions concerning the connection between the dynamic selection process and the outcomes of different sequences is discussed. The assumptions invoke different randomisation assumptions which are compatible with different selection regimes. Parametric forms are not involved. When participation in the sequences is decided every period depending on its success so far, the resulting endogeneity problem destroys nonparametric identification for many parameters of interest. However, some interesting dynamic forms of the average treatment effect are identified. As an empirical example for the application of this approach, we reexamine the effects of training programmes for the unemployed in West Germany.

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Article provided by Springer in its journal Empirical Economics.

Volume (Year): 39 (2010)
Issue (Month): 1 (August)
Pages: 111-137

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Handle: RePEc:spr:empeco:v:39:y:2010:i:1:p:111-137
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  1. Chamberlain, Gary, 1987. "Asymptotic efficiency in estimation with conditional moment restrictions," Journal of Econometrics, Elsevier, vol. 34(3), pages 305-334, March.
  2. Bruce D. Meyer, 1994. "Natural and Quasi- Experiments in Economics," NBER Technical Working Papers 0170, National Bureau of Economic Research, Inc.
  3. 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.
  4. Imbens, Guido W & Angrist, Joshua D, 1994. "Identification and Estimation of Local Average Treatment Effects," Econometrica, Econometric Society, vol. 62(2), pages 467-75, March.
  5. Behrman, Jere R & Sengupta, Piyali & Todd, Petra, 2005. "Progressing through PROGRESA: An Impact Assessment of a School Subsidy Experiment in Rural Mexico," Economic Development and Cultural Change, University of Chicago Press, vol. 54(1), pages 237-75, October.
  6. Michael Lechner, 2005. "A Note on Endogenous Control Variables in Evaluation Studies," University of St. Gallen Department of Economics working paper series 2005 2005-16, Department of Economics, University of St. Gallen.
  7. Jere R. Behrman & Yingmei Cheng & Petra E. Todd, 2004. "Evaluating Preschool Programs When Length of Exposure to the Program Varies: A Nonparametric Approach," The Review of Economics and Statistics, MIT Press, vol. 86(1), pages 108-132, February.
  8. Speckesser, Stefan & Fitzenberger, Bernd & Bergemann, Annette, 2004. "Evaluating the Dynamic Employment Effects of Training Programs in East Germany Using Conditional Difference-in-Differences," ZEW Discussion Papers 04-41, ZEW - Zentrum für Europäische Wirtschaftsforschung / Center for European Economic Research.
  9. Heckman, James, 2013. "Sample selection bias as a specification error," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 31(3), pages 129-137.
  10. Abbring, Jaap H., 2003. "Dynamic Econometric Program Evaluation," IZA Discussion Papers 804, Institute for the Study of Labor (IZA).
  11. Gerfin, Michael & Lechner, Michael, 2000. "Microeconometric Evaluation of the Active Labour Market Policy in Switzerland," IZA Discussion Papers 154, Institute for the Study of Labor (IZA).
  12. Lechner, Michael, 1999. "Identification and Estimation of Causal Effects of Multiple Treatments Under the Conditional Independence Assumption," IZA Discussion Papers 91, Institute for the Study of Labor (IZA).
  13. Michael Lechner, 2000. "Programme Heterogeneity and Propensity Score Matching: An Application to the Evaluation of Active Labour Market Policies," Econometric Society World Congress 2000 Contributed Papers 0647, Econometric Society.
  14. 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.
  15. Michael Lechner, 2004. "Sequential Matching Estimation of Dynamic Causal Models," University of St. Gallen Department of Economics working paper series 2004 2004-06, Department of Economics, University of St. Gallen.
  16. Charles F. Manski, 2004. "Social Learning from Private Experiences: The Dynamics of the Selection Problem," Review of Economic Studies, Wiley Blackwell, vol. 71(2), pages 443-458, 04.
  17. Lechner, Michael & Miquel, Ruth & Wunsch, Conny, 2004. "Long-Run Effects of Public Sector Sponsored Training in West Germany," IZA Discussion Papers 1443, Institute for the Study of Labor (IZA).
  18. Lechner, Michael, 1999. "Earnings and Employment Effects of Continuous Off-the-Job Training in East Germany after Unification," Journal of Business & Economic Statistics, American Statistical Association, vol. 17(1), pages 74-90, January.
  19. Ruth Miquel, 2003. "Identification of Effects of Dynamic Treatments with a Difference-in-Differences Approach," University of St. Gallen Department of Economics working paper series 2003 2003-06, Department of Economics, University of St. Gallen.
  20. Michael Lechner, 2002. "Program Heterogeneity And Propensity Score Matching: An Application To The Evaluation Of Active Labor Market Policies," The Review of Economics and Statistics, MIT Press, vol. 84(2), pages 205-220, May.
  21. 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.
  22. J.D. Angrist & Guido W. Imbens & D.B. Rubin, 1993. "Identification of Causal Effects Using Instrumental Variables," NBER Technical Working Papers 0136, National Bureau of Economic Research, Inc.
  23. Thomas Brodaty & Bruno Crépon & Denis Fougère, 2000. "Using Matching Estimators to Evaluate Alternative Youth Employment Programs : Evidence from France, 1986-1988," Working Papers 2000-25, Centre de Recherche en Economie et Statistique.
  24. Steven Lehrer & Weili Ding, 2004. "Estimating Dynamic Treatment Effects from Project STAR," Econometric Society 2004 North American Summer Meetings 252, Econometric Society.
  25. Guido W. Imbens, 1999. "The Role of the Propensity Score in Estimating Dose-Response Functions," NBER Technical Working Papers 0237, National Bureau of Economic Research, Inc.
  26. S. A. Murphy, 2003. "Optimal dynamic treatment regimes," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 65(2), pages 331-355.
  27. Edward Vytlacil, 2002. "Independence, Monotonicity, and Latent Index Models: An Equivalence Result," Econometrica, Econometric Society, vol. 70(1), pages 331-341, January.
  28. Hahn, Jinyong & Todd, Petra & Van der Klaauw, Wilbert, 2001. "Identification and Estimation of Treatment Effects with a Regression-Discontinuity Design," Econometrica, Econometric Society, vol. 69(1), pages 201-09, January.
  29. Chamberlain, Gary, 1982. "The General Equivalence of Granger and Sims Causality," Econometrica, Econometric Society, vol. 50(3), pages 569-81, May.
  30. Barbara Sianesi, 2004. "An Evaluation of the Swedish System of Active Labor Market Programs in the 1990s," The Review of Economics and Statistics, MIT Press, vol. 86(1), pages 133-155, February.
  31. Taber, Christopher R., 2000. "Semiparametric identification and heterogeneity in discrete choice dynamic programming models," Journal of Econometrics, Elsevier, vol. 96(2), pages 201-229, June.
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