Researchers from University of Toronto, MIT, and University of British Columbia published a new paper in the Journal of Marketing that aims to broaden the use and usefulness of quasi-experimental methods in marketing by describing the underlying logic and actions that make this work convincing.
The study, forthcoming in the Journal of Marketing, is titled “ Conducting Research in Marketing with Quasi-Experiments ” and is authored by Avi Goldfarb, Catherine Tucker, and Yanwen Wang.
Quasi-experimental methods have been widely applied in marketing to explain changes in consumer behavior, firm behavior, and market-level outcomes. As Goldfarb explains, “The purpose of quasi-experimental methods is to determine the presence of a causal relationship in the absence of experimental variation.”
As a vivid example, the authors describe a quasi-experiment that occurred when eBay shut down all the paid search advertising on Bing during a dispute with Microsoft, but lost little traffic. These quasi-experimental results inspired a follow-up field experiment where eBay randomized suspension of its branded paid search advertising and found results consistent with the quasi-experiment.
They begin by establishing various type of quasi-experimental variation at the individual, organizational, and market-levels. In each type, given the lack of an experiment, some individuals, companies, or markets receive an action or policy (i.e., treatment group) and some do not (i.e., control group). For example, some markets are affected by a new policy and some are not. The question is how the markets receiving the treatment would act if they had not received it (i.e., the counterfactual). Tucker explains that “The unobservability of the counterfactual means assumptions are required to ensure that differences, both observed and unobserved, are as untroubling as possible, thereby mimicking random assignment as closely as possible.”
The article discusses how to structure an empirical strategy to identify a causal relationship between using methods such as difference-in-differences, regression discontinuity, instrumental variables, propensity score matching, synthetic control, and selection bias correction. The authors emphasize the importance of clearly communicating identifying assumptions underlying the assertion of causality and establishing the generalizability of the findings.
The following topics are examined:
Full article and author contact information available at: https://doi.org/10.1177/00222429221082977
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Journal of Marketing
Conducting Research in Marketing with Quasi-Experiments
10-Feb-2022