We know that selection bias occurs when the treatment and control groups are not comparable, leading to differences in the outcome that are not solely due to the treatment.
How does one address the problem due to selection bias in a diff-in-diff setting? Or is DiD not suitable for any study that faces selection bias problem? What other econometric methods can give credible causal estimates of the intervention in the above case?
First edit: I think i didn't make my question precise. I am interested in effects of a policy on an outcome. The treatment group in my case is nonrandom (hence selection bias) in the sense that it is decided before the actual treatment based on their observed covariates like income. The interventation didn't create a treatment group; the group was formed based on its characteristics. How does one address this nonrandom-assignment-led selection bias? Is DiD useful? What are alternatives if not?