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Identification of average treatment effects in social experiments under different forms of attrition

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  • Martin Huber

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Abstract

As any empirical method used for causal analysis, social experiments are prone to attrition which may flaw the validity of the results. This paper considers the problem of partially missing outcomes in experiments. Firstly, it systematically reveals under which forms of attrition - in terms of its relation to observable and/or unobservable factors - experiments do (not) yield causal parameters. Secondly, it shows how the various forms of attrition can be controlled for by different methods of inverse probability weighting (IPW) that are tailored to the specific missing data problem at hand. In particular, it discusses IPW methods that incorporate instrumental variables when attrition is related to unobservables, which has been widely ignored in the experimental literature.

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File URL: http://www1.vwa.unisg.ch/RePEc/usg/dp2010/DP-1022-Hu.pdf
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Bibliographic Info

Paper provided by Department of Economics, University of St. Gallen in its series University of St. Gallen Department of Economics working paper series 2010 with number 2010-22.

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Length: 44 pages
Date of creation: Jun 2010
Date of revision:
Handle: RePEc:usg:dp2010:2010-22

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Keywords: experiments; attrition; inverse probability weighting;

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References

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  1. repec:att:wimass:9525 is not listed on IDEAS
  2. Joel L. Horowitz & Charles F. Manski, 1996. "Censoring of Outcomes and Regressors Due To Survey Nonresponse: Identification and Estimation Using Weights and Imputations," Econometrics 9602007, EconWPA, revised 06 Mar 1996.
  3. Kosuke Imai, 2009. "Statistical analysis of randomized experiments with non-ignorable missing binary outcomes: an application to a voting experiment," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 58(1), pages 83-104.
  4. Marianne Bertrand & Esther Duflo & Sendhil Mullainathan, 2004. "How Much Should We Trust Differences-in-Differences Estimates?," The Quarterly Journal of Economics, MIT Press, vol. 119(1), pages 249-275, February.
  5. Alan Krueger, 1997. "Experimental Estimates of Education Production Functions," Working Papers 758, Princeton University, Department of Economics, Industrial Relations Section..
  6. Guido W. Imbens, 2003. "Nonparametric Estimation of Average Treatment Effects under Exogeneity: A Review," NBER Technical Working Papers 0294, National Bureau of Economic Research, Inc.
  7. Paul Gertler, 2004. "Do Conditional Cash Transfers Improve Child Health? Evidence from PROGRESA's Control Randomized Experiment," American Economic Review, American Economic Association, vol. 94(2), pages 336-341, May.
  8. Joshua Angrist & Victor Lavy, 2009. "The Effects of High Stakes High School Achievement Awards: Evidence from a Randomized Trial," American Economic Review, American Economic Association, vol. 99(4), pages 1384-1414, September.
  9. Joshua Angrist & Eric Bettinger & Michael Kremer, 2006. "Long-Term Educational Consequences of Secondary School Vouchers: Evidence from Administrative Records in Colombia," American Economic Review, American Economic Association, vol. 96(3), pages 847-862, June.
  10. Ahn, Hyungtaik & Powell, James L., 1993. "Semiparametric estimation of censored selection models with a nonparametric selection mechanism," Journal of Econometrics, Elsevier, vol. 58(1-2), pages 3-29, July.
  11. Keisuke Hirano & Guido W. Imbens & Geert Ridder, 2000. "Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score," NBER Technical Working Papers 0251, National Bureau of Economic Research, Inc.
  12. Martin Huber, 2009. "Treatment evaluation in the presence of sample selection," University of St. Gallen Department of Economics working paper series 2009 2009-07, Department of Economics, University of St. Gallen.
  13. David S. Lee, 2009. "Training, Wages, and Sample Selection: Estimating Sharp Bounds on Treatment Effects," Review of Economic Studies, Oxford University Press, vol. 76(3), pages 1071-1102.
  14. Hausman, Jerry A & Wise, David A, 1979. "Attrition Bias in Experimental and Panel Data: The Gary Income Maintenance Experiment," Econometrica, Econometric Society, vol. 47(2), pages 455-73, March.
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Citations

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Cited by:
  1. Huber, Martin & Mellace, Giovanni, 2011. "Sharp bounds on causal effects under sample selection," Economics Working Paper Series 1134, University of St. Gallen, School of Economics and Political Science.
  2. Huber, Martin & Lechner, Michael & Steinmayr, Andreas, 2012. "Radius matching on the propensity score with bias adjustment: finite sample behaviour, tuning parameters and software implementation," Economics Working Paper Series 1226, University of St. Gallen, School of Economics and Political Science.
  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.
  4. Barbara Sianesi, 2013. "Dealing with randomisation bias in a social experiment exploiting the randomisation itself: the case of ERA," IFS Working Papers W13/15, Institute for Fiscal Studies.

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