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The Identity Fragmentation Bias

Tesary Lin, Sanjog Misra

arXiv 28 Aug 2020 · Econometrics · 3 citations (OpenAlex)

arXiv:2008.12849 · PDF · DOI · OpenAlex · Extracted main text

Abstract

Consumers interact with firms across multiple devices, browsers, and machines; these interactions are often recorded with different identifiers for the same consumer. The failure to correctly match different identities leads to a fragmented view of exposures and behaviors. This paper studies the identity fragmentation bias, referring to the estimation bias resulted from using fragmented data. Using a formal framework, we decompose the contributing factors of the estimation bias caused by data fragmentation and discuss the direction of bias. Contrary to conventional wisdom, this bias cannot be signed or bounded under standard assumptions. Instead, upward biases and sign reversals can occur even in experimental settings. We then compare several corrective measures, and discuss their respective advantages and caveats.

Citation extraction

33
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40
in-text mentions
33
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7,957
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appendix boundary found by appendix_command · 89% of the source is main text. Read the extracted text to check this.

Most heavily cited references

The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.

ReferenceIntensityMentionsSectionsMain text
1Dominic Coey \ Michael Bailey (2016) People and cookies: Imperfect treatment assignment in online experiments0.87452100%
2Thomas Blake, Sarah Moshary, Kane Sweeney \ Steven Tadelis Price salience and product choice0.64422100%
3Joel Barajas, Ram Akella, Marius Holtan \ Aaron Flores (2016) Experimental designs and estimation for online display advertising attribution in marketplaces0.51121100%
4Oliver J Rutz, Michael Trusov \ Randolph E Bucklin (2011) Modeling indirect effects of paid search advertising: Which keywords lead to more future visits?0.51121100%
5Ran Abramitzky, Leah Platt Boustan, Katherine Eriksson, James J Feig… Automated linking of historical data0.40511100%
6Arslan Aziz \ Rahul Telang (2016) What is a cookie worth?0.40511100%
7Martha Bailey, Connor Cole, Morgan Henderson \ Catherine Massey (2020) How well do automated methods perform in historical samples? Evidence from new ground truth0.40511100%
8Ron Berman (2018) Beyond the last touch: Attribution in online advertising0.40511100%
9Thomas Blake, Chris Nosko \ Steven Tadelis (2016) Returns to consumer search: Evidence from ebay0.40511100%
10Alexander Bleier \ Maik Eisenbeiss (2015) Personalized online advertising effectiveness: The interplay of what, when, and where0.40511100%

Showing the top 10 of 33 scored citations.