Difference‑in‑Differences (DID) estimates the causal effect of an intervention by comparing changes over time in a treated group to changes over time in a comparable control group.

Core Assumption: Parallel Trends

In the absence of treatment, treated and control groups would have followed similar trends. We test this using pre‑trend diagnostics and event‑study plots.

Designs

  • Single intervention date: classic two‑period DID or multi‑period DID
  • Staggered adoption: groups adopt at different times; use modern estimators

Estimators

  • Two‑way fixed effects (TWFE) for simple settings
  • Sun–Abraham or Callaway–Sant’Anna for staggered adoption

Diagnostics

  • Event‑study coefficients (pre‑treatment should be near zero)
  • Placebo periods where applicable
  • Robust SEs clustered at the unit or group level

Interpretation

Report the average treatment effect and show the time path of effects. State the population and period the estimate applies to and discuss sensitivity to specification.

Run DID on Your Data Back to Service Page