Lecture 22 Regression Discontinuity

Nick Huntington-Klein

March 17, 2019


  • We’ve been going over ways in which we can use control groups to isolate causal effects
  • We can select similar control groups using matching or controlling (what economists call “selection on observables”)
  • We can use a treated group at a different time as its own control group with fixed effects
  • When a treatment is applied at a particular time, we can select a reasonable control to account for the effects of time using difference-in-difference (a “natural experiment”)


  • We’re going to go over one other way in which we can find and isolate a very convincing control group
  • Like DID, it’s also an example of a natural experiment
  • Regression discontinuity

Regression Discontinuity

  • For regression discontinuity to work, we need the Treatment to be assigned based on a cutoff of what’s called a “running variable”
  • For example, imagine we want to know the effects of being in a Gifted and Talented (GATE) program on your adult earnings
  • Being admitted to the program is based on your test score (running variable)
  • If you score above 75, you’re in the program. 75 or below, you’re out!

Regression Discontinuity

  • Notice that the y-axis here is In GATE, not the outcome

Regression Discontinuity

  • Here’s how it look when we look at the actual outcome

Regression Discontinuity

  • Now, we have a bit of a problem!
  • If we look at the relationship between treatment and going to college, we’ll be picking up the fact that higher test scores make you more likely to go to college anyway