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Correcting Attrition Bias using Changes-in-Changes

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  • Dalia Ghanem
  • Sarojini Hirshleifer
  • D'esir'e K'edagni
  • Karen Ortiz-Becerra

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.

Suggested Citation

  • Dalia Ghanem & Sarojini Hirshleifer & D'esir'e K'edagni & Karen Ortiz-Becerra, 2022. "Correcting Attrition Bias using Changes-in-Changes," Papers 2203.12740, arXiv.org, revised Mar 2024.
    • Handle: RePEc:arx:papers:2203.12740
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    References listed on IDEAS

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