Synthetic Control Method (SCM)

A quantitative method that estimates the effect of an intervention on a single unit by constructing a weighted combination of similar untreated units.

A group of units that have not received the intervention being studied, but are similar to the treated unit in key characteristics.

A weighted combination of units in the donor pool designed to closely resemble the treated unit before the intervention.

The process of assigning weights to the donor pool units to make the synthetic control match the pre-intervention characteristics of the treated unit.

Assessing the impact of the intervention by comparing the post-intervention outcomes of the treated unit with those of the synthetic control.

The synthetic control should accurately reflect the treated unit's pre-intervention trends to ensure a good fit.

The intervention should not affect the units in the donor pool, ensuring they are truly independent.

Complete and detailed data on predictors and outcomes for both the treated unit and donor pool are crucial for accurate construction of the synthetic control.

Evaluating the effectiveness of public health policies like vaccination mandates by comparing regions with and without the policy.

Estimating the economic impact of events like natural disasters or policy changes by constructing a 'synthetic' version of the affected region without the event.

Transparency in SCM refers to the method's ability to clearly and explicitly show how the control group is constructed, including the weights assigned to each control unit.

Robustness in SCM means that the method is less likely to be affected by hidden factors (confounders) that might influence the outcome.

SCM allows for the visual comparison of the treated unit's outcomes with those of the synthetic control group, making it easier to see the potential impact of the intervention.

SCM requires extensive data about the treated unit and potential control units before the intervention took place, including information about characteristics and outcomes.

Choosing the right set of control units for the synthetic control is crucial; a poorly chosen donor pool can lead to inaccurate estimates.

The choice of predictors (characteristics used to match treated and control units) and the specific method used for calculating weights can significantly influence the results from SCM.

An example of applying SCM involves assessing the impact of a new tobacco control program in a country by comparing the country's post-intervention smoking rates to those of a synthetic control group created from countries with similar pre-intervention characteristics.

Choosing an appropriate donor pool involves selecting countries that share similar characteristics with the treated country before the intervention, such as smoking trends, socioeconomic factors, and relevant policies.