Introduction to Difference-in-differences Design

Abstract

Because it is difficult to conduct randomized controlled trials, observational studies are often used when evaluating the effects of health care policies. However, observational studies are subject to bias, such as a failure to eliminate the effects of trends in the outcome over time and permanent differences between treatment and control groups. The difference-indifferences design removes these biases by observing outcomes for the two groups at two time points. This article introduces the methods and assumptions for the difference-indifferences design and provides some examples of studies that have used this design. KEY WORDS Difference-indifferences design, parallel trends, common shocks I NTRO DU CT IO N Because it is difficult to conduct randomized controlled trials, observational studies are often used when evaluating the effects of health care policies. In this context, two study designs are considered: the before-and-after design and the cohort study design. The before-and-after design compares a group before the implementation of an intervention with another group after the implementation of the intervention to estimate the effect of the intervention. However, in the before-and-after design, it is not possible to determine whether the estimated effect is an actual effect of the intervention or a trend in the outcome over time [1, 2]. For example, when using a before-and-after design to examine the effects of a policy aimed at preventing passive smoking, it is not possible to eliminate the effects of a decreasing trend in the number of people smoking in public places that began before the policy was implemented. Conversely, cohort studies compare a group in a specific area where an intervention has been implemented with another group in a different area where the intervention has not been implemented. However, effects estimated in cohort studies are often biased toward permanent differences between the treatment and control groups. For example, lung cancer may be consistently more prevalent in areas that have implemented the intervention than in areas that have not, but the cohort study design is not equipped to eliminate the effects of this permanent difference. The difference-indifferences design removes the biases mentioned above by observing outcomes for two groups at two time points. This article introduces the methods and assumptions for difference-indifferences design and provides some examples of studies that have used this design.