We saw that our employee contribution example requires a sharp regression discontinuity design: all companies with at least 300 employees have a contribution matching program, and all companies with fewer than 300 employees do NOT have a contribution matching program. Before we decide which companies are similar enough to compare, we must consider some assumptions.

In order to get valid estimates of the treatment effect using a sharp RDD approach, several assumptions have to be met.

  1. The treatment variable impacts the outcome, but not any of the other variables.
  2. The treatment assignment happens only at ONE cutpoint value of the forcing variable.
  3. Treatment assignment is independent of the potential outcomes within a narrow interval around the cutpoint.
  4. Counterfactual outcomes can be modeled within the interval around the cutpoint.


Take a look at the learning environment and hover over each card to illustrate how each of these assumptions relates to the contribution matching program example.

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