Methods · Causal inference · Quasi experimental design

Difference in Differences (DiD)

Difference-in-differences (DiD) is a quasi-experimental method that estimates a treatment effect by comparing the change in outcomes over time in a treated group with the change in an untreated comparison group.

이중차분법(DiD)은 처치집단의 시간에 따른 결과 변화와 비처치 비교집단의 변화를 비교하여 처치효과를 추정하는 준실험 방법이다.

How do I estimate the effect of a policy using before-and-after data with a comparison group?

비교집단이 있는 정책 시행 전후 자료로 정책 효과를 어떻게 추정하나요?

Difference-in-differences subtracts the before-after change in a comparison group from the before-after change in the treated group, removing stable group differences and common time trends. It fits policy evaluations with outcomes observed before and after implementation for both affected and unaffected units.

When to use Difference in Differences (DiD)

When to use something else

Assumptions and requirements

What the analysis reports

How to report it

The difference-in-differences estimate indicated that [policy] changed [outcome] by [value] (cluster-robust SE = [value], p = [value]), and pre-treatment coefficients showed no significant differential trends.

Related methods

Run Difference in Differences (DiD) on Quorant

Data you need
a table (CSV or Excel) with an outcome variable, a treatment indicator and a time variable
Price
400 credits for one analysis up to 10,000 rows (complexity level 3); larger or wider data costs more. Optional methodologist review: 1800 credits.
Checking
Python results are cross-checked against R reference outputs in every deployment
Before you pay
A free pre-analysis diagnosis checks your data and gives an exact quote; nothing is deducted until you approve it.

Method ID Q81008 · Reviewed 2026-10-25