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

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  • Martin Huber

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.

Suggested Citation

  • Martin Huber, 2010. "Identification of average treatment effects in social experiments under different forms of attrition," University of St. Gallen Department of Economics working paper series 2010 2010-22, Department of Economics, University of St. Gallen.
    • Handle: RePEc:usg:dp2010:2010-22
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    File URL: http://ux-tauri.unisg.ch/RePEc/usg/dp2010/DP-1022-Hu.pdf
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    Cited by:

    1. Martin Huber & Giovanni Mellace, 2015. "Sharp Bounds on Causal Effects under Sample Selection," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 77(1), pages 129-151, February.
    2. Martin Huber & Michael Lechner & Andreas Steinmayr, 2015. "Radius matching on the propensity score with bias adjustment: tuning parameters and finite sample behaviour," Empirical Economics, Springer, vol. 49(1), pages 1-31, August.
    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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    More about this item

    Keywords

    experiments; attrition; inverse probability weighting;
    All these keywords.

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C93 - Mathematical and Quantitative Methods - - Design of Experiments - - - Field Experiments

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