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Avoiding Post-Treatment Bias in Audit Experiments

Published online by Cambridge University Press:  25 April 2018

Alexander Coppock
Alexander Coppock*
Affiliation:
Yale University e-mail: alex.coppock@yale.edu
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Extract

Audit experiments are used to measure discrimination in a large number of domains (Employment: Bertrand et al. (2004); Legislator responsiveness: Butler et al. (2011); Housing: Fang et al. (2018)). Audit studies all have in common that they estimate the average difference in response rates depending on randomly varied characteristics (such as the race or gender) of a requester. Scholars conducting audit experiments often seek to extend their analyses beyond the effect on response to the effects on the quality of the response. Response is a consequence of treatment; answering these important questions well is complicated by post-treatment bias (Montgomery et al., 2018). In this note, I consider a common form of post-treatment bias that occurs in audit experiments.

Information

Type
Short Report
Information
Journal of Experimental Political Science , Volume 6 , Issue 1 , Spring 2019 , pp. 1 - 4
Copyright
Copyright © The Experimental Research Section of the American Political Science Association 2018 

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Footnotes

The data, code, and any additional materials required to replicate all analyses in this article are available at the Journal of Experimental Political Science Dataverse within the Harvard Dataverse Network, at doi:10.7910/DVN/6NVI9C. I would like to thank Ariel White, Noah Nathan, Julie Faller, Saad Gulzar, and Peter Aronow for helpful comments.

References

REFERENCES

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Coppock Dataset

Dataset

https://doi.org/10.7910/DVN/6NVI9C
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