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 …

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 requires data measured from a treatment group and a control group at two or more different time periods, specifically at least one time period before "treatment" and …

Mar 4, 2025 · The Difference-in-Differences (DID) regression model helps answer this question by comparing trends between a group affected by the intervention (treatment group) and a similar …

Jul 23, 2025 · In this , we apply the Difference-in-Differences (DiD) method using OLS regression to estimate the effect of an intervention. We compare changes in outcomes between a …

The difference-in-difference (DID) technique originated in the field of econometrics, but the logic underlying the technique has been used as early as the 1850’s by John Snow and is called the …

Let’s illustrate the procedure for building and training a Difference-In-Differences regression model using an interesting real world example.

Because of the tremendous variations in design, data, and specification that practitioners encounter, we opt to focus on three of the most common aspects of modern DiD studies: the …

When an RCT is unavailable, then provided we observe enough covariates to eliminate all forms of selection and omitted variable bias, we can use regression to estimate accurate causal effects.

To apply Diff-in-Diff we need panel data and some (exogenous) change that affects a share of the observations in our sample, but not all of them, or at least not all at the same time.