GeoLift by Recast will soon begin supporting two different modeling algorithms to produce estimates of the incremental effect: Recast Bayesian Synthetic Control (REBA) and Basic (open source). REBA is our own custom, in-house Bayesian model while Basic is based off the open-source GeoLift and augsynth packages.
REBA
The Recast Bayesian Synthetic Control model is a fully Bayesian approach to synthetic control modeling. In testing various approaches, we have found this model produces better point estimates and more narrow confidence intervals with better coverage than both the Basic algorithm and other algorithms found in other open source packages. As a result, this is the default and our recommended approach, although we want to supply a more classical approach for those familiar with the open-source libraries.
Basic (Open-Source)
This algorithm is based off Meta’s open-source GeoLift R package, which uses another package called augsynth to fit the synthetic control models. Uncertainty quantification is done using permutation testing. One downside of this approach is that the confidence interval on the treatment effect is not guaranteed to (and frequently does not) contain the point estimate of the treatment effect. This algorithm has the advantage of being computationally faster, so may be preferred for large datasets.
Generating the original test market recommendations on the Design tab is currently implemented only with the Basic algorithm, although we hope to make this selectable in the future.