Difference-in-Differences (DiD) is a statistical technique used in econometrics and social sciences to estimate causal effects by comparing the changes in outcomes over time between a treatment group and a control group. This method helps to control for confounding factors that may influence the results, allowing researchers to identify the true impact of a treatment or intervention. It leverages data collected before and after a treatment is applied, making it especially useful in quasi-experimental designs where randomization is not feasible.
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