Identification of average treatment effects in social experiments under different forms of attrition
AbstractAs 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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Bibliographic InfoPaper 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.
Length: 44 pages
Date of creation: Jun 2010
Date of revision:
experiments; attrition; inverse probability weighting;
Find related papers by 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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- 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.
- 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.
- 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.
- 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.
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