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Qualifying control data with propensity score matching

Author

Listed:
  • Crisp, Dakota

    (Data Science Manager, RXA, USA)

  • Kristo, Matt

    (Director of Analytics, Outsell, USA)

  • Everest, Courtney

    (Data Scientist, RXA, USA)

  • King, Jenna

    (Data Scientist, RXA, USA)

  • Brehmer, Emily

    (Data Scientist, RXA, USA)

  • Barnes, Danielle

    (Director of Data Science, RXA, USA)

  • Prantner, Jonathan

    (Chief Analytics Officer, RXA, USA)

Abstract

The Fourth Industrial Revolution has brought with it a proliferation of data and an environment with ever-increasing complexity. While experimental design is the gold standard in assessing direct causal impact, the need for frequent business pivots and the abundance of pre-existing data makes quasi-experimental design a notable contender. Propensity score matching is one such quasi-experimental design tool that enables retrospective hypothesis testing, enabling businesses to use previously unviable data. This paper provides a case study of how this technique helps process nonrandomised data into viable analyses.

Suggested Citation

  • Crisp, Dakota & Kristo, Matt & Everest, Courtney & King, Jenna & Brehmer, Emily & Barnes, Danielle & Prantner, Jonathan, 2023. "Qualifying control data with propensity score matching," Applied Marketing Analytics: The Peer-Reviewed Journal, Henry Stewart Publications, vol. 9(1), pages 30-38, June.
    • Handle: RePEc:aza:ama000:y:2023:v:9:i:1:p:30-38
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    More about this item

    Keywords

    propensity score matching; lift analysis; quasi-experimental design; automotive; control group; design of experiments;
    All these keywords.

    JEL classification:

    • M3 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising

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