RDD uses a sharp threshold for treatment assignment
Image: Johannes Vermeer, Public domain, via Wikimedia Commons
Regression discontinuity design
RDD uses a sharp threshold for treatment assignment
Regression discontinuity designs (RDD) rely on a sharp cutoff or threshold to assign treatments. Observations just above and below this threshold are compared to estimate the treatment effect. This method helps in situations where random assignment is not possible.
Example
In evaluating scholarship programs, students scoring just above or below a certain GPA threshold are assigned to receive scholarships or not, respectively. Researchers then compare outcomes of students near this cutoff to estimate the scholarship's impact.
RDD provides a way to infer causal effects in quasi-experimental settings, which is crucial for fields like economics and psychology where randomization is often impractical.
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