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{{short description|Statistical technique to use observational data for causal analysis}}
{{short description|Statistical technique to use observational data for causal analysis}}
==General definition==
==General definition==
[[File:Illustration of Difference in Differences.png|thumb|upright=1.3]]
[[File:Illustration of Difference in Differences.png|thumb|upright=1.3|链接=Special:FilePath/Illustration_of_Difference_in_Differences.png]]
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 at least one time period after "treatment." In the example pictured, the outcome in the treatment group is represented by the line P and the outcome in the control group is represented by the line S. The outcome (dependent) variable in both groups is measured at time 1, before either group has received the treatment (i.e., the independent or explanatory variable), represented by the points ''P''<sub>1</sub> and ''S''<sub>1</sub>. The treatment group then receives or experiences the treatment and both groups are again measured at time 2. Not all of the difference between the treatment and control groups at time 2 (that is, the difference between ''P''<sub>2</sub> and ''S''<sub>2</sub>) can be explained as being an effect of the treatment, because the treatment group and control group did not start out at the same point at time 1. DID therefore calculates the "normal" difference in the outcome variable between the two groups (the difference that would still exist if neither group experienced the treatment), represented by the dotted line ''Q''. (Notice that the slope from ''P''<sub>1</sub> to ''Q'' is the same as the slope from ''S''<sub>1</sub> to ''S''<sub>2</sub>.) The treatment effect is the difference between the observed outcome (P<sub>2</sub>) and the "normal" outcome (the difference between P<sub>2</sub> and Q).
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 at least one time period after "treatment." In the example pictured, the outcome in the treatment group is represented by the line P and the outcome in the control group is represented by the line S. The outcome (dependent) variable in both groups is measured at time 1, before either group has received the treatment (i.e., the independent or explanatory variable), represented by the points ''P''<sub>1</sub> and ''S''<sub>1</sub>. The treatment group then receives or experiences the treatment and both groups are again measured at time 2. Not all of the difference between the treatment and control groups at time 2 (that is, the difference between ''P''<sub>2</sub> and ''S''<sub>2</sub>) can be explained as being an effect of the treatment, because the treatment group and control group did not start out at the same point at time 1. DID therefore calculates the "normal" difference in the outcome variable between the two groups (the difference that would still exist if neither group experienced the treatment), represented by the dotted line ''Q''. (Notice that the slope from ''P''<sub>1</sub> to ''Q'' is the same as the slope from ''S''<sub>1</sub> to ''S''<sub>2</sub>.) The treatment effect is the difference between the observed outcome (P<sub>2</sub>) and the "normal" outcome (the difference between P<sub>2</sub> and Q).
= = 假设 = =
= = 假设 = =
[[File:Parallel Trend Assumption.png|right|thumb|320px| Illustration of the parallel trend assumption]]
[[File:Parallel Trend Assumption.png|right|thumb|320px| Illustration of the parallel trend assumption|链接=Special:FilePath/Parallel_Trend_Assumption.png]]
right|thumb|320px| Illustration of the parallel trend assumption
right|thumb|320px| Illustration of the parallel trend assumption