Correcting Attrition Bias using Changes-in-Changes

Dalia Ghanem, Sarojini Hirshleifer, D\'esir\'e K\'edagni and Karen Ortiz-Becerra
Authors registered in the RePEc Author Service: Desire Kedagni

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Abstract: Attrition is a common and potentially important threat to internal validity in treatment effect studies. We extend the changes-in-changes approach to identify the average treatment effect for respondents and the entire study population in the presence of attrition. Our method, which exploits baseline outcome data, can be applied to randomized experiments as well as quasi-experimental difference-in-difference designs. A formal comparison highlights that while widely used corrections typically impose restrictions on whether or how response depends on treatment, our proposed attrition correction exploits restrictions on the outcome model. We further show that the conditions required for our correction can accommodate a broad class of response models that depend on treatment in an arbitrary way. We illustrate the implementation of the proposed corrections in an application to a large-scale randomized experiment.

Date: 2022-03, Revised 2024-03
New Economics Papers: this item is included in nep-ecm and nep-exp
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Citations: View citations in EconPapers (1)

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Journal Article: Correcting attrition bias using changes-in-changes (2024)
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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2203.12740

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