ABSTRACT
In this chapter, the author presents a relatively complete data analysis. Insurance redlining refers to the practice of refusing to issue insurance to certain types of people or within some geographic area. However, can the insurance companies claim that the discrepancy is due to greater risks in some zip codes? The insurance companies could claim that they were denying insurance in neighborhoods where they had sustained large fire-related losses and any discriminatory effect was a by-product of legitimate business practice. For the present data, suppose that the effect of adjusting for income differences was to remove the race effect. This would pose an interesting, but non-statistical, question. The investigation would then have become more complex, because the peoples would need to consider more deeply which covariates should be adjusted for and which should not. Such a discussion is beyond the scope of this book but illustrates why causal inference is a difficult subject.
