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Title: Graphical Causal Modelling: an Application to Identify and Estimate Cause-and-Effect Relationships
Resulting in 1 citation.
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Burnett, J. Wesley Blackwell, Calvin |
Graphical Causal Modelling: an Application to Identify and Estimate Cause-and-Effect Relationships Applied Economics published online (7 May 2023): DOI: 10.1080/00036846.2023.2208856. Also: https://www.tandfonline.com/doi/full/10.1080/00036846.2023.2208856 Cohort(s): NLSY79 Publisher: Taylor & Francis Keyword(s): College Degree; High School Completion/Graduates; Modeling; Propensity Scores; Student Loans / Student Aid 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 enrollment 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. |
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Bibliography Citation
Burnett, J. Wesley and Calvin Blackwell. "Graphical Causal Modelling: an Application to Identify and Estimate Cause-and-Effect Relationships." Applied Economics published online (7 May 2023): DOI: 10.1080/00036846.2023.2208856.
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