To avoid a long discussion in the comments, here are some thoughts for you that can help uncover what is going on.
Countervailing effects of explanatory variables: This is perfectly normal when the variables are not measuring the same latent concept, even if they are highly correlated. For example, I am currently modeling grant placement by county (similar exercise as what you are doing). When I include median income, income has a positive relationship with grant placement. When I add population to the model (which has a moderate correlation with income), the effect of income becomes negative. Why? Because now I'm controlling for population. Before a 10% increase in income didn't hold anything else constant, so when income rises, so does population, and income was picking up both the effect of income and population. With pop in the model, an increase in income no longer picks up the population effect. You situation is likely similar. Without knowing more about the variables, I can't help you figure out if it makes sense, but this is the type of conceptual thinking you need to do to figure out if the modeling makes sense or not.
Interaction terms: I recommend you do some more digging on how interaction terms work. Interaction terms are going to be correlated with both components; and it is not uncommon because of that for an interaction to absorb the variance of the component parts. Don't think about interaction terms like you do other variables (where you worry about collinearity). The point of an interaction term is to identify if the effect of one variable is moderated by another variable. In your case, a policy intervention is likely to affect outcomes differently depending on other characteristics. Not just region in the country, but the specific characteristics of the counties. If the interaction term is significant, and you can interpret it, and the overall model fit improves, it is new information for you to consider. If you are using nonlinear models like logit, Poisson, etc., interactions can be tricky but easier to interpret with marginal effects.
Endogeneity: Though you don't provide a lot of detail on the policy intervention, I think you need to assume that the intervention may be a result, not a cause, of the dependent variables. That means a standard regression (be it linear, logit, etc.) will be biased. There are many different types of models you can look into that will control for this endogeneity - they have different assumptions and data requirements. Here are some examples:
- Propensity score matching
- Instrumental variables (2SLS)
- Control functions (similar to instrumental variables)
- Selection models (such as Heckman's selection model)
- Time series/panel models, where you can clearly control for the temporal independence of the intervention and the effects
You will have a hard time convincing researchers of your results if you don't consider endogeneity.