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What is Difference-in-Differences (DiD) in causal inference?
in Data Science & Statistics by Gold Status (31,925 points) | 125 views

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Difference-in-Differences (DiD) design combines both before-after and treatment-control comparison when doing causal inference.

The problems that DiD solves are:

  • A treatment-control comparison is not necessarily a causal comparison because of the potential systematic differences between two groups
  • A unit is arguably the “best match” for itself
  • A before-after comparison (of the same units) is not necessarily a causal comparison because of the potential change in time

 

by Gold Status (31,925 points)

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