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Approximation for Standardized Difference #26

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sprivite opened this issue Jun 27, 2024 · 1 comment
Open

Approximation for Standardized Difference #26

sprivite opened this issue Jun 27, 2024 · 1 comment
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@sprivite
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sprivite commented Jun 27, 2024

The linear optimizer cannot do standardized mean difference, but there is a good approximation it can use, namely:

SMD = (mean(pool) - mean(target))/ (sqrt(var(pool) + var(target))
\approx (mean(pool) - mean(target))/ (sqrt(2 var(target))= (absolute mean difference) / (sqrt(2 var(target))

When distance(pool, target) >> 1, this is a poor approximation, but the approximation becomes increasingly good as pool --> target, which is anyway what the optimizer is doing.

Implement this as an option to ConstraintSatisfactionMatcher

@sprivite sprivite self-assigned this Jun 27, 2024
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This only works when the target size is fixed to the input target size.

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