DID is a quasi-experimental design that makes use of longitudinal data from treatment and control groups to obtain an appropriate counterfactual to estimate a causal effect.

Difference in differences (DID[1] or DD[2]) is a statistical technique used in econometrics and quantitative research in the social sciences that attempts to mimic an experimental research …

Aug 1, 2022 · In this chapter, we will study the Difference-In-Differences regression model. The DID model is a powerful and flexible regression technique that can be used to estimate the …

Apr 21, 2025 · Difference-in-Differences (DiD) is a widely used statistical technique to estimate the effect of a treatment or intervention by comparing the changes in outcomes over time …

Differences-in-Differences regression (DID) is used to asses the causal effect of an event by comparing the set of units where the event happened (treatment group) in relation to units …

Difference-in-differences (DiD) approaches are applied in situations when certain groups are exposed to a treatment and others are not. The logic of DiD is best explained with an example …

DiD is a combination of time-series difference (compares outcomes across pre-treatment and post-treatment periods) and cross-sectional difference (compares outcomes between …

Users can install this module by typing “ssc install diff” in the Stata command window. This module allows researchers to reduce the selection bias problem by calculating the kernel …

Regression models with fixed effects are the primary workhorse for causal inference with panel data Researchers use them to adjust for unobserved time-invariant confounders (omitted …

What is the scope of a difference-in-difference model? What is the right design to apply a difference-in-difference model? How should you organize your data before using a difference …