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Regression Discontinuity Design

In: Demystifying Causal Inference

Author

Listed:
  • Vikram Dayal

    (Institute of Economic Growth)

  • Anand Murugesan

    (Central European University)

Abstract

Consider two students, one scoring 89 and the other scoring 90 on a causal inference course. Are they so different after all? Our hunch is they are not so different as the slight difference in their score could be due to random error or temporary factors like fatigue or fever during the exam. However, in many universities, a student with a score of 89 would receive a B plus, while a score of 90 would secure an A. This one-point difference resulting in an A or B can have real-world consequences, as it can be a deciding factor for scholarship eligibility or admission to a university. In such scenarios, the Regression Discontinuity Design (RDD) method comes into play.

Suggested Citation

  • Vikram Dayal & Anand Murugesan, 2023. "Regression Discontinuity Design," Springer Books, in: Demystifying Causal Inference, chapter 0, pages 169-192, Springer.
    • Handle: RePEc:spr:sprchp:978-981-99-3905-3_9
    DOI: 10.1007/978-981-99-3905-3_9
    as

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