Is data partitioning necessary for an explanatory model and why?
An explanatory model is used to identify and explain what causes some particular outcome (i.e. identify/quantify the drivers of effect).

Even though the model will not be used for prediction, it needs to be accurate enough to adequately predict the response so that you can be sure the conclusions you draw from the model are valid. As an extreme example, if your explanatory model does not predict the outcome at all, then all the inferences you make from this model are useless.

So then, how does one validate model accuracy? In most cases, data partitioning and validation on the hold-out sample is used; in your case the validation is more of a sense-check for accuracy.