Data, measurement, and causal inferences in machine learning: opportunities and challenges for marketing

ABSTRACT The emergence of digital data and the methods used to analyze them are revolutionizing marketing research. The vast quantity of data offers marketing researchers countless opportunities to better predict and potentially explain consumer behavior. Yet, as we will argue in this paper, marketing researchers should not prematurely abandon cognitive and methodological procedures that have been refined during centuries of philosophical and scientific thought. Merging the literatures from various hard sciences, we discuss recent challenges in data management and measurement in the era of digital data and the role of machine learning in causal inference.

• Publication year

  2021

• Venue

  Journal of Marketing Theory and Practice

• Publication date

  2021-01-02

• Fields of study

  Not labeled

• Identifiers
  DOI 10.1080/10696679.2020.1860683
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

• No references are available for this paper.

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