Graphical causal modelling: an application to identify and estimate cause-and-effect relationships

James Burnett and Calvin Blackwell

Applied Economics, 2024, vol. 56, issue 33, 3986-4000

Abstract: This paper offers an accessible discussion of graphical causal models and how such a framework can be used to help identify causal relations. A graphical causal model represents a researcher’s qualitative assumptions. As a result of the credibility revolution, there is growing interest to properly estimate cause-and-effect relationships. Using several examples, we illustrate how graphical models can and cannot be used to identify causation from observational data. Further, we offer a replication of a previous study that explored college enrolment by high school seniors who were eligible for student aid. From the original study, we use a graphical causal model to motivate the quantitative and qualitative modelling assumptions. Using a similar difference-in-difference approach based on propensity score matching, we estimate a smaller average treatment effect than the original study. The smaller estimated effect arguably stems from the graphical causal model’s delineation of the original model specification.

Date: 2024
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DOI: 10.1080/00036846.2023.2208856

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