The Identity Fragmentation Bias

TL;DR

This paper investigates the bias caused by fragmented consumer identity data across multiple devices, revealing complex biases that can distort behavioral estimates and evaluating correction methods.

Contribution

It provides a formal framework to analyze identity fragmentation bias, showing it can cause unpredictable biases including upward bias and sign reversals.

Findings

Bias can be unbounded and unpredictable

Standard correction methods have varying effectiveness

Experimental settings can also exhibit bias reversals

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.