Qualifying control data with propensity score matching

Dakota Crisp, Matt Kristo, Courtney Everest, Jenna King, Emily Brehmer, Danielle Barnes and Jonathan Prantner
Additional contact information
Dakota Crisp: Data Science Manager, RXA, USA
Matt Kristo: Director of Analytics, Outsell, USA
Courtney Everest: Data Scientist, RXA, USA
Jenna King: Data Scientist, RXA, USA
Emily Brehmer: Data Scientist, RXA, USA
Danielle Barnes: Director of Data Science, RXA, USA
Jonathan Prantner: Chief Analytics Officer, RXA, USA

Applied Marketing Analytics: The Peer-Reviewed Journal, 2023, vol. 9, issue 1, 30-38

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.

Keywords: propensity score matching; lift analysis; quasi-experimental design; automotive; control group; design of experiments (search for similar items in EconPapers)
JEL-codes: M3 (search for similar items in EconPapers)
Date: 2023
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