Ensuring Model Feasibility

Michael Robbins and Steven Davenport


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.

Causes of model infeasibility

Model infeasibility becomes increasingly likely when:

Responses to an infeasible model

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

Specification of matching variables

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

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

Parameters for calculating weights

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

Calling on (computationally-intensive) back-up models

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).