  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.
