Difference-in-Differences

Photo by Alex Kondratiev on Unsplash
  1. The allocation of treatment or control to population groups is completely randomized to maintain the statistical significance of the causal effect of the treatment.
  2. The treatment and control groups have parallel trends in the pre-treatment period. This is important to analyze the change in behavior of the treatment group if treated as compared to the behavior of the treatment group if not treated.
  3. The assumption of SUTVA should be strictly followed between the population groups, i.e., there should be strictly no spillovers between population groups.
  4. The population groups remain stable (no flux) to maintain the repeatability of the experiment design.
DiD estimation, graphical estimation
  1. Time = 1 means post-treatment time period and 0 means pre-treatment time period.
  2. Treatment = 1 for treatment group and 0 for control group.
  3. Covariates = all other independent variables and interaction terms.

--

--

Get the Medium app

A button that says 'Download on the App Store', and if clicked it will lead you to the iOS App store
A button that says 'Get it on, Google Play', and if clicked it will lead you to the Google Play store