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Difference-in-Difference-in-Differences (DDD), also called triple differences, is a quasi-experimental design that extends the standard Difference-in-Differences (DiD) approach by incorporating a second dimension of variation. While DiD compares outcome changes over time between treated and untreated groups, DDD introduces an additional grouping variable (such as region, industry, or policy subtype) and relies on the interaction of three factors: time, treatment, and the third dimension. The goal is to further control for unobserved shocks that vary across both time and the extra dimension.
In practice, DDD relies on a parallel trends assumption that is conditional on the third dimension. That
A typical specification can be written with fixed effects and covariates, where the key term is the
Applications include evaluating policies implemented differently across regions and industries, or programs targeted by multiple criteria.