# Avoiding multicollinearity with dummy encoding of ordinal variable

I was having trouble finding this exact circumstance; hopefully I haven't missed an obvious previous answer.

I have a target variable, Y (discrete counts), and two potential regressors, A (discrete counts) and B (continuous). There's an obvious, necessary causal relationship A -> B from the data generating process, and B is in fact highly correlated with A. (R ~= .95)

My aim in the analysis is to determine whether B is a significant factor in addition to A, so the collinearity is problematic. My first pass (feel free to suggest principled alternatives if this seems kafka; not a stats wiz yet) is to fit a negative binomial regression essentially as Y ~ A + B, and determine whether the value fit to B has a significant value.

However, the collinearity makes the regression suspect if the ordinal variable A is treated as a continuous variable. In this circumstance, is it acceptable and useful to treat A as a categorical variable using dummy encoding?

At a glance, this seems to ensure B can't be collinear with any level of A. But that seems suspiciously like a free lunch to me - am I missing something that may be problematic here?

(I am aware of dummy trap, but unless I'm mistaken, that seems like a separate and easily avoided encoding issue...)