So, basicaly, me and my team are trying to target our marketing campaigns based on logistic regression's coefficients. The idea is to understand the dimensions that increase or decrease the probability of an user click in the campaign's ads. Through a logistic regression, we concluded that we can achieve statistical significance and we can isolate the effects of each dimension of targeting. That being said, our main goal is the impact of the independent variables and their relationship with the dependent variable, not prediction. The problem is that the dependent variable is a rare event. We already have the model and it' has many coefficients that are statistical significant, but my concern is if they can be biased because of the unbalanced data. I have seen many topics that recomend the King and Zeng method : https://www.cambridge.org/core/journals/political-analysis/article/div-classtitlelogistic-regression-in-rare-events-datadiv/1E09F0F36F89DF12A823130FDF0DA462. But also, I have seen answers that argue that since the Logistic Regressions is a probabilistic model, it's not affected by rare event data and it's coefficients are fine.
King, Gary, and Langche Zeng. "Logistic regression in rare events data." Political analysis 9.2 (2001): 137-163.