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

  • Martin Huber


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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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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  1. Dean Karlan & John List, 2006. "Does price matter in charitable giving? Evidence from a large-scale natural field experiment," Natural Field Experiments 00279, The Field Experiments Website.
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  4. Martin Huber, 2014. "Treatment Evaluation in the Presence of Sample Selection," Econometric Reviews, Taylor & Francis Journals, vol. 33(8), pages 869-905, November.
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  7. Guido W. Imbens, 2009. "Better LATE Than Nothing: Some Comments on Deaton (2009) and Heckman and Urzua (2009)," NBER Working Papers 14896, National Bureau of Economic Research, Inc.
  8. 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.
  9. 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.
  10. Alan B. Krueger, 1999. "Experimental Estimates Of Education Production Functions," The Quarterly Journal of Economics, MIT Press, vol. 114(2), pages 497-532, May.
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  12. 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.
  13. 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.
  14. 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.
  15. 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.
  16. 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.
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