Identification of average treatment effects in social experiments under different forms of attrition
Martin Huber ()
University of St. Gallen Department of Economics working paper series 2010 from Department of Economics, University of St. Gallen
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.
Keywords: experiments; attrition; inverse probability weighting (search for similar items in EconPapers)
JEL-codes: C21 C93 (search for similar items in EconPapers)
Pages: 44 pages
New Economics Papers: this item is included in nep-ecm
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Persistent link: https://EconPapers.repec.org/RePEc:usg:dp2010:2010-22
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