IDEAS home Printed from https://ideas.repec.org/a/spr/empeco/v39y2010i1p111-137.html
   My bibliography  Save this article

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

Author

Abstract

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.
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Michael Lechner & Ruth Miquel, 2010. "Identification of the effects of dynamic treatments by sequential conditional independence assumptions," Empirical Economics, Springer, vol. 39(1), pages 111-137, August.
  • Handle: RePEc:spr:empeco:v:39:y:2010:i:1:p:111-137
    DOI: 10.1007/s00181-009-0297-3
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s00181-009-0297-3
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1007/s00181-009-0297-3?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to look for a different version below or search for a different version of it.

    Other versions of this item:

    References listed on IDEAS

    as
    1. 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, June.
    2. 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.
    3. Jaap Abbring & James Heckman, 2008. "Dynamic policy analysis," CeMMAP working papers CWP05/08, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    4. 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.
    5. Michael Lechner & Ruth Miquel & Conny Wunsch, 2011. "Long‐Run Effects Of Public Sector Sponsored Training In West Germany," Journal of the European Economic Association, European Economic Association, vol. 9(4), pages 742-784, August.
    6. Lechner, Michael, 1999. "Identification and Estimation of Causal Effects of Multiple Treatments Under the Conditional Independence Assumption," IZA Discussion Papers 91, Institute of Labor Economics (IZA).
    7. Annette Bergemann & Bernd Fitzenberger & Stefan Speckesser, 2009. "Evaluating the dynamic employment effects of training programs in East Germany using conditional difference-in-differences," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(5), pages 797-823.
    8. Heckman, James J. & Navarro, Salvador, 2007. "Dynamic discrete choice and dynamic treatment effects," Journal of Econometrics, Elsevier, vol. 136(2), pages 341-396, February.
    9. Brodaty, Thomas & Crépon, Bruno & Fougère, Denis, 2000. "Using Matching Estimators to Evaluate Alternative Youth Employment Programs: Evidence from France, 1986-1988," CEPR Discussion Papers 2604, C.E.P.R. Discussion Papers.
    10. Michael Gerfin & Michael Lechner, 2002. "A Microeconometric Evaluation of the Active Labour Market Policy in Switzerland," Economic Journal, Royal Economic Society, vol. 112(482), pages 854-893, October.
    11. 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, May.
    12. Chamberlain, Gary, 1982. "The General Equivalence of Granger and Sims Causality," Econometrica, Econometric Society, vol. 50(3), pages 569-581, May.
    13. 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.
    14. Imbens, Guido W & Angrist, Joshua D, 1994. "Identification and Estimation of Local Average Treatment Effects," Econometrica, Econometric Society, vol. 62(2), pages 467-475, March.
    15. Abbring, Jaap H., 2003. "Dynamic Econometric Program Evaluation," IZA Discussion Papers 804, Institute of Labor Economics (IZA).
    16. 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.
    17. 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.
    18. 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.
    19. 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.
    20. László Mátyás & Patrick Sevestre (ed.), 2008. "The Econometrics of Panel Data," Advanced Studies in Theoretical and Applied Econometrics, Springer, number 978-3-540-75892-1.
    21. Meyer, Bruce D, 1995. "Natural and Quasi-experiments in Economics," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(2), pages 151-161, April.
    22. 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.
    23. 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.
    24. 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.
    25. Bruno Crepon & Francis Kramarz, 2002. "Employed 40 Hours or Not Employed 39: Lessons from the 1982 Mandatory Reduction of the Workweek," Journal of Political Economy, University of Chicago Press, vol. 110(6), pages 1355-1389, December.
    26. Patrick Sevestre & Laszlo Matyas, 2008. "The Econometrics of Panel Data," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-00279977, HAL.
    27. Chamberlain, Gary, 1987. "Asymptotic efficiency in estimation with conditional moment restrictions," Journal of Econometrics, Elsevier, vol. 34(3), pages 305-334, March.
    28. 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.
    29. 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.
    30. Heckman, James, 2013. "Sample selection bias as a specification error," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 31(3), pages 129-137.
    31. 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.
    32. Joshua D. Angrist, 1998. "Estimating the Labor Market Impact of Voluntary Military Service Using Social Security Data on Military Applicants," Econometrica, Econometric Society, vol. 66(2), pages 249-288, March.
    33. 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 201-209, January.
    34. 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 237-275, October.
    35. A. D. Roy, 1951. "Some Thoughts On The Distribution Of Earnings," Oxford Economic Papers, Oxford University Press, vol. 3(2), pages 135-146.
    36. Steven Lehrer & Weili Ding, 2004. "Estimating Dynamic Treatment Effects from Project STAR," Econometric Society 2004 North American Summer Meetings 252, Econometric Society.
    37. Jaap H. Abbring & Gerard J. van den Berg, 2003. "The Nonparametric Identification of Treatment Effects in Duration Models," Econometrica, Econometric Society, vol. 71(5), pages 1491-1517, September.
    38. Charles F. Manski, 2004. "Social Learning from Private Experiences: The Dynamics of the Selection Problem," Review of Economic Studies, Oxford University Press, vol. 71(2), pages 443-458.
    39. Joshua 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.
    40. Edward Vytlacil, 2002. "Independence, Monotonicity, and Latent Index Models: An Equivalence Result," Econometrica, Econometric Society, vol. 70(1), pages 331-341, January.
    41. Chamberlain, Gary, 1992. "Sequential Moment Restrictions in Panel Data: Comment," Journal of Business & Economic Statistics, American Statistical Association, vol. 10(1), pages 20-26, January.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Guido W. Imbens & Jeffrey M. Wooldridge, 2009. "Recent Developments in the Econometrics of Program Evaluation," Journal of Economic Literature, American Economic Association, vol. 47(1), pages 5-86, March.
    2. Lechner Michael & Miquel Ruth & Wunsch Conny, 2007. "The Curse and Blessing of Training the Unemployed in a Changing Economy: The Case of East Germany After Unification," German Economic Review, De Gruyter, vol. 8(4), pages 468-509, December.
    3. Michael Lechner & Ruth Miquel & Conny Wunsch, 2011. "Long‐Run Effects Of Public Sector Sponsored Training In West Germany," Journal of the European Economic Association, European Economic Association, vol. 9(4), pages 742-784, August.
    4. Lechner, Michael, 2004. "Sequential Matching Estimation of Dynamic Causal Models," IZA Discussion Papers 1042, Institute of Labor Economics (IZA).
    5. Lechner, Michael, 2013. "Treatment effects and panel data," Economics Working Paper Series 1314, University of St. Gallen, School of Economics and Political Science.
    6. Frölich, Markus & Lechner, Michael, 2010. "Exploiting Regional Treatment Intensity for the Evaluation of Labor Market Policies," Journal of the American Statistical Association, American Statistical Association, vol. 105(491), pages 1014-1029.
    7. Bernd Fitzenberger & Stefan Speckesser, 2007. "Employment effects of the provision of specific professional skills and techniques in Germany," Empirical Economics, Springer, vol. 32(2), pages 529-573, May.
    8. Flores-Lagunes, Alfonso & Gonzalez, Arturo & Neumann, Todd C., 2007. "Estimating the Effects of Length of Exposure to a Training Program: The Case of Job Corps," IZA Discussion Papers 2846, Institute of Labor Economics (IZA).
    9. Stephan, Gesine & Pahnke, André, 2008. "The Relative Effectiveness of Selected Active Labour Market Programmes and the Common Support Problem," IZA Discussion Papers 3767, Institute of Labor Economics (IZA).
    10. Biewen, Martin & Fitzenberger, Bernd & Osikominu, Aderonke & Waller, Marie, 2007. "Which Program for Whom? Evidence on the Comparative Effectiveness of Public Sponsored Training Programs in Germany," IZA Discussion Papers 2885, Institute of Labor Economics (IZA).
    11. Michael Lechner & Conny Wunsch, 2006. "Active Labour Market Policy in East Germany: Waiting for the Economy to Take Off," University of St. Gallen Department of Economics working paper series 2006 2006-24, Department of Economics, University of St. Gallen.
    12. Michael Lechner, 2002. "Some practical issues in the evaluation of heterogeneous labour market programmes by matching methods," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 165(1), pages 59-82, February.
    13. Rahul Singh & Liyuan Xu & Arthur Gretton, 2020. "Kernel Methods for Causal Functions: Dose, Heterogeneous, and Incremental Response Curves," Papers 2010.04855, arXiv.org, revised Aug 2022.
    14. Gerard J. van den Berg & Petyo Bonev & Enno Mammen, 2020. "Nonparametric Instrumental Variable Methods for Dynamic Treatment Evaluation," The Review of Economics and Statistics, MIT Press, vol. 102(2), pages 355-367, May.
    15. Deborah A. Cobb‐Clark & Thomas Crossley, 2003. "Econometrics for Evaluations: An Introduction to Recent Developments," The Economic Record, The Economic Society of Australia, vol. 79(247), pages 491-511, December.
    16. Bernd Fitzenberger & Olga Orlanski & Aderonke Osikominu & Marie Paul, 2013. "Déjà Vu? Short-term training in Germany 1980–1992 and 2000–2003," Empirical Economics, Springer, vol. 44(1), pages 289-328, February.
    17. Jochen Kluve & Hilmar Schneider & Arne Uhlendorff & Zhong Zhao, 2012. "Evaluating continuous training programmes by using the generalized propensity score," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 175(2), pages 587-617, April.
    18. Dettmann, E. & Becker, C. & Schmeißer, C., 2011. "Distance functions for matching in small samples," Computational Statistics & Data Analysis, Elsevier, vol. 55(5), pages 1942-1960, May.
    19. Lechner, Michael, 2008. "A note on endogenous control variables in causal studies," Statistics & Probability Letters, Elsevier, vol. 78(2), pages 190-195, February.
    20. Lechner, Michael, 2008. "Long-Run Labour Market Effects of Individual Sports Activities," IZA Discussion Papers 3559, Institute of Labor Economics (IZA).

    More about this item

    Keywords

    Labor market effects of training programs; Dynamic treatment regimes; Nonparametric identification; Causal effects; Sequential randomization; Program evaluation; Treatment effects; Dynamic matching; Panel data; C21; C31; J68;
    All these keywords.

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:empeco:v:39:y:2010:i:1:p:111-137. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: . General contact details of provider: http://www.springer.com .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.