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One possible solution to the separation problem is to make the model fit less perfectly by reducing the number of variables or effects in the model, categorizing continuous variables, or merging categories of categorical (CLASS) variables. It is often difficult to know exactly which variables cause the separation, but variables that exhibit large parameter estimates or standard errors are likely candidates.
Because separation often happens with small data sets, another possible solution is to use exact logistic regression, which is available via the EXACT statement in PROC LOGISTIC. However, exact methods are very computationally intensive and can take great amounts of time and/or memory even for moderate sized problems. A penalized likelihood method by Firth can often converge to a solution even though a unique solution by maximum likelihood does not exist in separation cases. Firth's method is available, beginning in SAS 9.2, by specifying the FIRTH option in the MODEL statement.
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