Understand syn how to#

Synthetic control methodology (SCM) allows the construction of a counterfactual by selecting a weighted average of the outcome variable from a group of units similar to the treated unit.ДЄbadie and Gardeazabal 1 first set out SCM in a study looking at the impact of terrorism on the economy of the Basque region of Spain. It is sometimes difficult to establish whether the parallel trends assumption is met and whether the control group is a sufficiently accurate representation of what would have happened in the treated area without the intervention. If pretreatment trends are not parallel, or an event occurred that affected only one of the units, some of the difference between the units will be the difference in the trend rather than the effect of the intervention, and the estimate of the treatment effect will be biased. If the difference between the treated and control unit changes in the postintervention period, this can be interpreted as a treatment effect. Provided that treated and control units had parallel pretreatment trends, and there were no other events affecting one unit but not the other, a constant difference would be expected to continue in the postintervention period. DiD estimates the treatment effect by taking the difference between the change in the treated unit and the change in the control units in the periods before and after the intervention.

When a control unit is added, the change in the treated unit is compared with the change in the control in what is known as a вЂdifference in differenceвЂ™ (DiD) approach. Here, the modelled continuation of the pretrend is the counterfactual. Other approaches, such as uncontrolled вЂbefore and afterвЂ™ approaches, rely on the assumption that the previous trend in the treated unit would have continued but for the intervention only. Regression discontinuity analysis uses individuals on the opposite side of a cut-off as the control group, while the instrumental variable technique uses a variable that influences the explanatory variable but not the dependent variable, thereby allowing a researcher to separate the intervention effect. Some methods, such as direct matching and propensity score matching, select controls based only on observed characteristics. In establishing a counterfactual, different study designs use different methods to mimic the trend in the outcome variable in the absence of the intervention. In order to minimise bias (ie, the risk that the effect seen is due to factors other than the intervention), many studies use a control group or counterfactual. Providing that suitable methods have been adopted to ensure the internal validity of the study, observational studies have much to contribute. Non-randomised approaches using observational data are, therefore, the only methods left open to evaluators. Often, randomised approaches to evaluation are not possible for population-level health interventions for practical, political or ethical reasons, for example, where a smoking ban is introduced across the entire population of a country. They may be specific to healthcare or involve the regulation of or changes to sectors such as education, transport, housing or employment.

Population-level health interventions aim to improve health by changing underlying social, economic and environmental conditions, or by directly influencing health behaviours. From our review, we noted that the synthetic control method has been underused in public health.

Traditional statistical inference is not appropriate with this approach, although alternatives are available.

Understand syn how to#

There is currently no consensus on what constitutes a вЂgood fitвЂ™ or how to judge similarity. It is critical that the synthetic control is built from a pool of potential controls that are similar to the treated unit. If a good preimplementation fit is established over an extended period of time, a discrepancy in the outcome variable following the intervention can be interpreted as an intervention effect. The credibility of the result relies on achieving a good preimplementation fit for the outcome of interest between treated unit and synthetic control. Results Advantages of the synthetic control method are that it offers an approach suitable when there is a small number of treated units and control units and it does not rely on parallel preimplementation trends like difference in difference methods.