Using microSynth is easy when the models used to calculate weights are feasible. But as more variables are used for matching, especially when data is scarce or variables are sparse, the risk of an infeasible model increases. Below is a quick guide to how to troubleshoot model feasiblity issues.

Model infeasibility becomes increasingly likely when:

- There are few control observations
- More variables used for matching
- Matching variables are sparse (e.g., mostly zero)
- Treatment units have extreme values for matching variables
- Permutation weights are calculated in addition to main weights
- Jackknife weights are calculated in addition to main weights

As there are multiple causes of model infeasibility, there is an equally broad range of responses.

If a model is found to be infeasible, the problem may trace back to matching variable specification. We recommend the following diagnostic steps:

- Review the frequency of matching variables (e.g., with
`hist()`

or`table()`

) to check for sparseness. Sparse variables are difficult to match on without large sample sizes. - Compare the distribution of variable values in treatment units to the un-treated units.
- Attempt to reduce the number of matching variables, move variables
from exact matches (
`match.out`

/`match.covar`

) to best-possible matches (`match.out.min`

/`match.covar.min`

), or aggregate time-variant variables before matching.

When attempts to match on a sparse variable cause model infeasibility, there are several solutions:

- Do not attempt an exact match. If the variable is time-invariant,
move it from
`match.covar`

to`match.covar.min`

; if the variable is time-variant, move it from`match.out`

to`match.out.min`

. - If the variable is time-variant, aggregate the variable over
multiple time periods before matching. If just one or several variables
that appear to be sparse or for which the treatment contains values that
are rare in the un-treated units, then the user can issue instructions
for each of those variables to be aggregated over different time
periods. (Those time periods do not have to be at regular intervals, for
instance if the sparseness only occurs at certain points in the
pre-intervention data.) Exercise 4 from the provides an example of this.
If the user would like to aggregate all time-variant variables over the
same regular time periods, then it is somewhat simple to pass
`match.out`

or`match.out.min`

a vector of variable names, and specify the aggregation periods using`period`

.

If varying the specification of matching variables is not satisfactory, the user can set the parameters microSynth() uses for the calculation of weights.

`max.mse`

may be raised. This relaxes the constraint governing matches for variables passed to`match.out`

and`match.covar`

.- Advanced users may wish to alter
`maxit`

,`cal.epsilon`

,`calfun`

, and`bounds`

, which correspond to parameters from the`survey::calibrate()`

and govern the calculation of weights.

By default microSynth() attempts to calculate weights using simple
methods. But because these are not always sufficient to produce a
feasible model, two arguments, `check.feas`

and
`use.backup`

, specify how microsynth should find and use less
restrictive backup models. The two arguments do not interact and can be
set independently.

`check.feas = TRUE`

will search for a single model that
yields satisfactory constraints for all purposes: estimating main
weights, permutation weights, and jackknife residuals. The same model
will be used for all purposes.

`use.backup = TRUE`

will calculate the main weights
without checking for feasibility, but if weights appear to be poor
(i.e., they do not satisfy the max.mse condition), then weights will be
re-calculated using another model. This way, different backup models may
be used for different purposes (i.e., for estimating main weights,
permutation weights, and jackknife residuals).