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Economic Analysis of the Digital Economy$
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Avi Goldfarb, Shane M. Greenstein, and Catherine E. Tucker

Print publication date: 2015

Print ISBN-13: 9780226206844

Published to Chicago Scholarship Online: September 2015

DOI: 10.7208/chicago/9780226206981.001.0001

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PRINTED FROM CHICAGO SCHOLARSHIP ONLINE (www.chicago.universitypressscholarship.com). (c) Copyright University of Chicago Press, 2020. All Rights Reserved. An individual user may print out a PDF of a single chapter of a monograph in CHSO for personal use.date: 05 April 2020

Estimation of Treatment Effects from Combined Data

Estimation of Treatment Effects from Combined Data

Identification versus Data Security

Chapter:
(p.279) 10 Estimation of Treatment Effects from Combined Data
Source:
Economic Analysis of the Digital Economy
Author(s):

Tatiana Komarova

Denis Nekipelov

Evgeny Yakovlev

Publisher:
University of Chicago Press
DOI:10.7208/chicago/9780226206981.003.0010

The security of sensitive individual data is a subject of indisputable importance. One of the majorthreats to sensitive data arises when one can link sensitive information and publicly available data. In this paper the authors demonstrate that even if the sensitive data are never publicly released, the pointestimates from the empirical model estimated from the combined public and sensitive data may leadto a disclosure of individual information. Their theory builds on the work in Komarova, Nekipelovand Yakovlev (2011) where they analyze the individual disclosure that arises from the releases ofmarginal empirical distributions of individual data. The disclosure threat in that case is posed bythe possibility of a linkage between the released marginal distributions. In this chapter, they analyze adifferent type of disclosure. Namely, they use the notion of the risk of statistical partial disclosure tomeasure the threat from the inference on sensitive individual attributes from the released empiricalmodel that uses the data combined from the public and private sources. As the main example the authors consider a treatment effect model in which the treatment status of an individual constitutes sensitiveinformation.

Keywords:   data protection, model identification, data combination

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