Regression Discontinuity Design

Vikram Dayal () and Anand Murugesan ()
Additional contact information
Vikram Dayal: Institute of Economic Growth
Anand Murugesan: Central European University

Chapter Chapter 9 in Demystifying Causal Inference, 2023, pp 169-192 from Springer

Abstract: 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.

Keywords: Regression discontinuity; Minimum legal drinking age; Term limits; Rural roads (search for similar items in EconPapers)
Date: 2023
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-981-99-3905-3_9

Ordering information: This item can be ordered from
http://www.springer.com/9789819939053

DOI: 10.1007/978-981-99-3905-3_9

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().